Journal articles: 'Point cloud instance segmentation'

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Zhao, Lin, and Wenbing Tao. "JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12951–58. http://dx.doi.org/10.1609/aaai.v34i07.6994.

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In this paper, we propose a novel joint instance and semantic segmentation approach, which is called JSNet, in order to address the instance and semantic segmentation of 3D point clouds simultaneously. Firstly, we build an effective backbone network to extract robust features from the raw point clouds. Secondly, to obtain more discriminative features, a point cloud feature fusion module is proposed to fuse the different layer features of the backbone network. Furthermore, a joint instance semantic segmentation module is developed to transform semantic features into instance embedding space, and then the transformed features are further fused with instance features to facilitate instance segmentation. Meanwhile, this module also aggregates instance features into semantic feature space to promote semantic segmentation. Finally, the instance predictions are generated by applying a simple mean-shift clustering on instance embeddings. As a result, we evaluate the proposed JSNet on a large-scale 3D indoor point cloud dataset S3DIS and a part dataset ShapeNet, and compare it with existing approaches. Experimental results demonstrate our approach outperforms the state-of-the-art method in 3D instance segmentation with a significant improvement in 3D semantic prediction and our method is also beneficial for part segmentation. The source code for this work is available at https://github.com/dlinzhao/JSNet.

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Agapaki, Eva, and Ioannis Brilakis. "Instance Segmentation of Industrial Point Cloud Data." Journal of Computing in Civil Engineering 35, no. 6 (November 2021): 04021022. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000972.

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Liu, Hui, Ciyun Lin, Dayong Wu, and Bowen Gong. "Slice-Based Instance and Semantic Segmentation for Low-Channel Roadside LiDAR Data." Remote Sensing 12, no. 22 (November 21, 2020): 3830. http://dx.doi.org/10.3390/rs12223830.

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More and more scholars are committed to light detection and ranging (LiDAR) as a roadside sensor to obtain traffic flow data. Filtering and clustering are common methods to extract pedestrians and vehicles from point clouds. This kind of method ignores the impact of environmental information on traffic. The segmentation process is a crucial part of detailed scene understanding, which could be especially helpful for locating, recognizing, and classifying objects in certain scenarios. However, there are few studies on the segmentation of low-channel (16 channels in this paper) roadside 3D LiDAR. This paper presents a novel segmentation (slice-based) method for point clouds of roadside LiDAR. The proposed method can be divided into two parts: the instance segmentation part and semantic segmentation part. The part of the instance segmentation of point cloud is based on the regional growth method, and we proposed a seed point generation method for low-channel LiDAR data. Furthermore, we optimized the instance segmentation effect under occlusion. The part of semantic segmentation of a point cloud is realized by classifying and labeling the objects obtained by instance segmentation. For labeling static objects, we represented and classified a certain object through the related features derived from its slices. For labeling moving objects, we proposed a recurrent neural network (RNN)-based model, of which the accuracy could be up to 98.7%. The result implies that the slice-based method can obtain a good segmentation effect and the slice has good potential for point cloud segmentation.

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Gao, Zhiyong, and Jianhong Xiang. "Real-time 3D Object Detection Using Improved Convolutional Neural Network Based on Image-driven Point Cloud." (Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 14, no. 8 (December 23, 2021): 826–36. http://dx.doi.org/10.2174/2352096514666211026142721.

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Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN comprises the frustum sequence module, 3D instance segmentation module SNET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module ENET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved Convolutional Neural Network (CNN) based on image-driven point clouds.

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Karara, Ghizlane, Rafika Hajji, and Florent Poux. "3D Point Cloud Semantic Augmentation: Instance Segmentation of 360° Panoramas by Deep Learning Techniques." Remote Sensing 13, no. 18 (September 13, 2021): 3647. http://dx.doi.org/10.3390/rs13183647.

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Semantic augmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionised image segmentation and classification, its impact on point cloud is an active research field. In this paper, we propose an instance segmentation and augmentation of 3D point clouds using deep learning architectures. We show the potential of an indirect approach using 2D images and a Mask R-CNN (Region-Based Convolution Neural Network). Our method consists of four core steps. We first project the point cloud onto panoramic 2D images using three types of projections: spherical, cylindrical, and cubic. Next, we homogenise the resulting images to correct the artefacts and the empty pixels to be comparable to images available in common training libraries. These images are then used as input to the Mask R-CNN neural network, designed for 2D instance segmentation. Finally, the obtained predictions are reprojected to the point cloud to obtain the segmentation results. We link the results to a context-aware neural network to augment the semantics. Several tests were performed on different datasets to test the adequacy of the method and its potential for generalisation. The developed algorithm uses only the attributes X, Y, Z, and a projection centre (virtual camera) position as inputs.

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Cao, Yu, Yancheng Wang, Yifei Xue, Huiqing Zhang, and Yizhen Lao. "FEC: Fast Euclidean Clustering for Point Cloud Segmentation." Drones 6, no. 11 (October 27, 2022): 325. http://dx.doi.org/10.3390/drones6110325.

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Segmentation from point cloud data is essential in many applications, such as remote sensing, mobile robots, or autonomous cars. However, the point clouds captured by the 3D range sensor are commonly sparse and unstructured, challenging efficient segmentation. A fast solution for point cloud instance segmentation with small computational demands is lacking. To this end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies a point-wise scheme over the cluster-wise scheme used in existing works. The proposed method avoids traversing every point constantly in each nested loop, which is time and memory-consuming. Our approach is conceptually simple, easy to implement (40 lines in C++), and achieves two orders of magnitudes faster against the classical segmentation methods while producing high-quality results.

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Li, Dawei, Jinsheng Li, Shiyu Xiang, and Anqi Pan. "PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants." Plant Phenomics 2022 (May 23, 2022): 1–20. http://dx.doi.org/10.34133/2022/9787643.

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Phenotyping of plant growth improves the understanding of complex genetic traits and eventually expedites the development of modern breeding and intelligent agriculture. In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. In this work, we first proposed the Voxelized Farthest Point Sampling (VFPS), a novel point cloud downsampling strategy, to prepare our plant dataset for training of deep neural networks. Then, a deep learning network—PSegNet, was specially designed for segmenting point clouds of several species of plants. The effectiveness of PSegNet originates from three new modules including the Double-Neighborhood Feature Extraction Block (DNFEB), the Double-Granularity Feature Fusion Module (DGFFM), and the Attention Module (AM). After training on the plant dataset prepared with VFPS, the network can simultaneously realize the semantic segmentation and the leaf instance segmentation for three plant species. Comparing to several mainstream networks such as PointNet++, ASIS, SGPN, and PlantNet, the PSegNet obtained the best segmentation results quantitatively and qualitatively. In semantic segmentation, PSegNet achieved 95.23%, 93.85%, 94.52%, and 89.90% for the mean Prec, Rec, F1, and IoU, respectively. In instance segmentation, PSegNet achieved 88.13%, 79.28%, 83.35%, and 89.54% for the mPrec, mRec, mCov, and mWCov, respectively.

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Zhao, Guangyuan, Xue Wan, Yaolin Tian, Yadong Shao, and Shengyang Li. "3D Component Segmentation Network and Dataset for Non-Cooperative Spacecraft." Aerospace 9, no. 5 (May 1, 2022): 248. http://dx.doi.org/10.3390/aerospace9050248.

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Spacecraft component segmentation is one of the key technologies which enables autonomous navigation and manipulation for non-cooperative spacecraft in OOS (On-Orbit Service). While most of the studies on spacecraft component segmentation are based on 2D image segmentation, this paper proposes spacecraft component segmentation methods based on 3D point clouds. Firstly, we propose a multi-source 3D spacecraft component segmentation dataset, including point clouds from lidar and VisualSFM (Visual Structure From Motion). Then, an improved PointNet++ based 3D component segmentation network named 3DSatNet is proposed with a new geometrical-aware FE (Feature Extraction) layers and a new loss function to tackle the data imbalance problem which means the points number of different components differ greatly, and the density distribution of point cloud is not uniform. Moreover, when the partial prior point clouds of the target spacecraft are known, we propose a 3DSatNet-Reg network by adding a Teaser-based 3D point clouds registration module to 3DSatNet to obtain higher component segmentation accuracy. Experiments carried out on our proposed dataset demonstrate that the proposed 3DSatNet achieves 1.9% higher instance mIoU than PointNet++_SSG, and the highest IoU for antenna in both lidar point clouds and visual point clouds compared with the popular networks. Furthermore, our algorithm has been deployed on an embedded AI computing device Nvidia Jetson TX2 which has the potential to be used on orbit with a processing speed of 0.228 s per point cloud with 20,000 points.

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Huang, Pin-Hao, Han-Hung Lee, Hwann-Tzong Chen, and Tyng-Luh Liu. "Text-Guided Graph Neural Networks for Referring 3D Instance Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1610–18. http://dx.doi.org/10.1609/aaai.v35i2.16253.

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This paper addresses a new task called referring 3D instance segmentation, which aims to segment out the target instance in a 3D scene given a query sentence. Previous work on scene understanding has explored visual grounding with natural language guidance, yet the emphasis is mostly constrained on images and videos. We propose a Text-guided Graph Neural Network (TGNN) for referring 3D instance segmentation on point clouds. Given a query sentence and the point cloud of a 3D scene, our method learns to extract per-point features and predicts an offset to shift each point toward its object center. Based on the point features and the offsets, we cluster the points to produce fused features and coordinates for the candidate objects. The resulting clusters are modeled as nodes in a Graph Neural Network to learn the representations that encompass the relation structure for each candidate object. The GNN layers leverage each object's features and its relations with neighbors to generate an attention heatmap for the input sentence expression. Finally, the attention heatmap is used to "guide" the aggregation of information from neighborhood nodes. Our method achieves state-of-the-art performance on referring 3D instance segmentation and 3D localization on ScanRefer, Nr3D, and Sr3D benchmarks, respectively.

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Zhang, Yongjun, Wangshan Yang, Xinyi Liu, Yi Wan, Xianzhang Zhu, and Yuhui Tan. "Unsupervised Building Instance Segmentation of Airborne LiDAR Point Clouds for Parallel Reconstruction Analysis." Remote Sensing 13, no. 6 (March 17, 2021): 1136. http://dx.doi.org/10.3390/rs13061136.

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Efficient building instance segmentation is necessary for many applications such as parallel reconstruction, management and analysis. However, most of the existing instance segmentation methods still suffer from low completeness, low correctness and low quality for building instance segmentation, which are especially obvious for complex building scenes. This paper proposes a novel unsupervised building instance segmentation (UBIS) method of airborne Light Detection and Ranging (LiDAR) point clouds for parallel reconstruction analysis, which combines a clustering algorithm and a novel model consistency evaluation method. The proposed method first divides building point clouds into building instances by the improved kd tree 2D shared nearest neighbor clustering algorithm (Ikd-2DSNN). Then, the geometric feature of the building instance is obtained using the model consistency evaluation method, which is used to determine whether the building instance is a single building instance or a multi-building instance. Finally, for multiple building instances, the improved kd tree 3D shared nearest neighbor clustering algorithm (Ikd-3DSNN) is used to divide multi-building instances again to improve the accuracy of building instance segmentation. Our experimental results demonstrate that the proposed UBIS method obtained good performances for various buildings in different scenes such as high-rise building, podium buildings and a residential area with detached houses. A comparative analysis confirms that the proposed UBIS method performed better than state-of-the-art methods.

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Poux, F., and J. J. Ponciano. "SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 309–16. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-309-2020.

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Abstract. Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning.

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Tian, Yan, Yujie Zhang, Wei-Gang Chen, Dongsheng Liu, Huiyan Wang, Huayi Xu, Jianfeng Han, and Yiwen Ge. "3D Tooth Instance Segmentation Learning Objectness and Affinity in Point Cloud." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 4 (November 30, 2022): 1–16. http://dx.doi.org/10.1145/3504033.

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Digital dentistry has received more attention in the past decade. However, current deep learning-based methods still encounter difficult challenges. The proposal-based methods are sensitive to the localization results due to the lack of local cues, while the proposal-free methods have poor clustering outputs because of the affinity measured by the low-level characteristics, especially in situations of tightly arranged teeth. In this article, we present a novel proposal-based approach to combine objectness and pointwise knowledge in an attention mechanism for point cloud-based tooth instance segmentation, using local information to improve 3D proposal generation and measuring the importance of local points by calculating the center distance. We evaluate the performance of our approach by constructing a Shining3D tooth instance segmentation dataset. The experimental results verify that our approach gives competitive results when compared with the other available approaches.

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Rosu, Radu Alexandru, Peer Schütt, Jan Quenzel, and Sven Behnke. "LatticeNet: fast spatio-temporal point cloud segmentation using permutohedral lattices." Autonomous Robots 46, no. 1 (October 19, 2021): 45–60. http://dx.doi.org/10.1007/s10514-021-09998-1.

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AbstractDeep convolutional neural networks have shown outstanding performance in the task of semantically segmenting images. Applying the same methods on 3D data still poses challenges due to the heavy memory requirements and the lack of structured data. Here, we propose LatticeNet, a novel approach for 3D semantic segmentation, which takes raw point clouds as input. A PointNet describes the local geometry which we embed into a sparse permutohedral lattice. The lattice allows for fast convolutions while keeping a low memory footprint. Further, we introduce DeformSlice, a novel learned data-dependent interpolation for projecting lattice features back onto the point cloud. We present results of 3D segmentation on multiple datasets where our method achieves state-of-the-art performance. We also extend and evaluate our network for instance and dynamic object segmentation.

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Jones, R. Kenny, Aalia Habib, and Daniel Ritchie. "SHRED." ACM Transactions on Graphics 41, no. 6 (November 30, 2022): 1–11. http://dx.doi.org/10.1145/3550454.3555440.

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We present SHRED, a method for 3D SHape REgion Decomposition. SHRED takes a 3D point cloud as input and uses learned local operations to produce a segmentation that approximates fine-grained part instances. We endow SHRED with three decomposition operations: splitting regions, fixing the boundaries between regions, and merging regions together. Modules are trained independently and locally, allowing SHRED to generate high-quality segmentations for categories not seen during training. We train and evaluate SHRED with fine-grained segmentations from PartNet; using its merge-threshold hyperparameter, we show that SHRED produces segmentations that better respect ground-truth annotations compared with baseline methods, at any desired decomposition granularity. Finally, we demonstrate that SHRED is useful for downstream applications, out-performing all baselines on zero-shot fine-grained part instance segmentation and few-shot finegrained semantic segmentation when combined with methods that learn to label shape regions.

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Wang, Peng-Shuai, Yu-Qi Yang, Qian-Fang Zou, Zhirong Wu, Yang Liu, and Xin Tong. "Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 2773–81. http://dx.doi.org/10.1609/aaai.v35i4.16382.

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We propose an unsupervised method for learning a generic and efficient shape encoding network for different shape analysis tasks. Our key idea is to jointly encode and learn shape and point features from unlabeled 3D point clouds. For this purpose, we adapt HRNet to octree-based convolutional neural networks for jointly encoding shape and point features with fused multiresolution subnetworks and design a simple-yet-efficient Multiresolution Instance Discrimination (MID) loss for jointly learning the shape and point features. Our network takes a 3D point cloud as input and output both shape and point features. After training, Our network is concatenated with simple task-specific back-ends and fine-tuned for different shape analysis tasks. We evaluate the efficacy and generality of our method with a set of shape analysis tasks, including shape classification, semantic shape segmentation, as well as shape registration tasks. With simple back-ends, our network demonstrates the best performance among all unsupervised methods and achieves competitive performance to supervised methods. For fine-grained shape segmentation on the PartNet dataset, our method even surpasses existing supervised methods by a large margin.

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Yu, Lijie, Yuliang Sun, Xudong Zhang, Yongwei Miao, and Xiuli Zhang. "Point Cloud Instance Segmentation of Indoor Scenes Using Learned Pairwise Patch Relations." IEEE Access 9 (2021): 15891–901. http://dx.doi.org/10.1109/access.2021.3051618.

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Xu, Yajun, Satoshi Kanai, Hiroaki Date, and Tomoaki Sano. "Deep-Learning-Based Three-Dimensional Detection of Individual Wave-Dissipating Blocks from As-Built Point Clouds Measured by UAV Photogrammetry and Multibeam Echo-Sounder." Remote Sensing 14, no. 21 (November 4, 2022): 5575. http://dx.doi.org/10.3390/rs14215575.

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Wave-dissipating blocks are the armor elements of breakwaters that protect beaches, ports, and harbors from erosion by waves. Monitoring the poses of individual wave-dissipating blocks benefits the accuracy of the block supplemental work plan, recording of the construction status, and monitoring of long-term pose change in blocks. This study proposes a deep-learning-based approach to detect individual blocks from large-scale three-dimensional point clouds measured with a pile of wave-dissipating blocks placed overseas and underseas using UAV photogrammetry and a multibeam echo-sounder. The approach comprises three main steps. First, the instance segmentation using our originally designed deep convolutional neural network partitions an original point cloud into small subsets of points, each corresponding to an individual block. Then, the block-wise 6D pose is estimated using a three-dimensional feature descriptor, point cloud registration, and CAD models of blocks. Finally, the type of each segmented block is identified using model registration results. The results of the instance segmentation on real-world and synthetic point cloud data achieved 70–90% precision and 50–76% recall with an intersection of union threshold of 0.5. The pose estimation results on synthetic data achieved 83–95% precision and 77–95% recall under strict pose criteria. The average block-wise displacement error was 30 mm, and the rotation error was less than 2∘. The pose estimation results on real-world data showed that the fitting error between the reconstructed scene and the scene point cloud ranged between 30 and 50 mm, which is below 2% of the detected block size. The accuracy in the block-type classification on real-world point clouds reached about 95%. These block detection performances demonstrate the effectiveness of our approach.

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Vetrivel, A., M. Gerke, N. Kerle, and G. Vosselman. "Segmentation of UAV-based images incorporating 3D point cloud information." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W2 (March 10, 2015): 261–68. http://dx.doi.org/10.5194/isprsarchives-xl-3-w2-261-2015.

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Numerous applications related to urban scene analysis demand automatic recognition of buildings and distinct sub-elements. For example, if LiDAR data is available, only 3D information could be leveraged for the segmentation. However, this poses several risks, for instance, the in-plane objects cannot be distinguished from their surroundings. On the other hand, if only image based segmentation is performed, the geometric features (e.g., normal orientation, planarity) are not readily available. This renders the task of detecting the distinct sub-elements of the building with similar radiometric characteristic infeasible. In this paper the individual sub-elements of buildings are recognized through sub-segmentation of the building using geometric and radiometric characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV) images are used for inferring the geometric characteristics of roofs and facades of the building. However, the image-based 3D points are noisy, error prone and often contain gaps. Hence the segmentation in 3D space is not appropriate. Therefore, we propose to perform segmentation in image space using geometric features from the 3D point cloud along with the radiometric features. The initial detection of buildings in 3D point cloud is followed by the segmentation in image space using the region growing approach by utilizing various radiometric and 3D point cloud features. The developed method was tested using two data sets obtained with UAV images with a ground resolution of around 1-2 cm. The developed method accurately segmented most of the building elements when compared to the plane-based segmentation using 3D point cloud alone.

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Zhu, Xiaojun, Zheng Zhang, Jian Ruan, Houde Liu, and Hanxu Sun. "ResSANet: Learning Geometric Information for Point Cloud Processing." Sensors 21, no. 9 (May 6, 2021): 3227. http://dx.doi.org/10.3390/s21093227.

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Point clouds with rich local geometric information have potentially huge implications in several applications, especially in areas of robotic manipulation and autonomous driving. However, most point cloud processing methods cannot extract enough geometric features from a raw point cloud, which restricts the performance of their downstream tasks such as point cloud classification, shape retrieval and part segmentation. In this paper, the authors propose a new method where a convolution based on geometric primitives is adopted to accurately represent the elusive shape in the form of a point cloud to fully extract hidden geometric features. The key idea of the proposed approach is building a brand-new convolution net named ResSANet on the basis of geometric primitives to learn hierarchical geometry information. Two different modules are devised in our network, Res-SA and ResSA2, to achieve feature fusion at different levels in ResSANet. This work achieves classification accuracy up to 93.2% on the ModelNet40 dataset and the shape retrieval with an effect of 87.4%. The part segmentation experiment also achieves an accuracy of 83.3% (class mIoU) and 85.3% (instance mIoU) on ShapeNet dataset. It is worth mentioning that the number of parameters in this work is just 1.04 M while the network depth is minimal. Experimental results and comparisons with state-of-the-art methods demonstrate that our approach can achieve superior performance.

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Behley, Jens, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Jürgen Gall, and Cyrill Stachniss. "Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset." International Journal of Robotics Research 40, no. 8-9 (April 20, 2021): 959–67. http://dx.doi.org/10.1177/02783649211006735.

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A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene completion for predictive reasoning on the semantics and occluded regions; and (3) panoptic segmentation combining point-wise classification and assigning individual instance identities to separate objects of the same class. In this article, we provide details on our dataset showing an unprecedented number of fully annotated point cloud sequences, more information on our labeling process to efficiently annotate such a vast amount of point clouds, and lessons learned in this process. The dataset and resources are available at http://www.semantic-kitti.org .

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Zong, Chengjie, Hao Wang, and ZhiboWan. "An improved 3D point cloud instance segmentation method for overhead catenary height detection." Computers & Electrical Engineering 98 (March 2022): 107685. http://dx.doi.org/10.1016/j.compeleceng.2022.107685.

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Singer, Nina, and Vijayan K. Asari. "View-Agnostic Point Cloud Generation for Occlusion Reduction in Aerial Lidar." Remote Sensing 14, no. 13 (June 21, 2022): 2955. http://dx.doi.org/10.3390/rs14132955.

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Occlusions are one of the leading causes of data degradation in lidar. The presence of occlusions reduces the overall aesthetic quality of a point cloud, creating a signature that is specific to that viewpoint and sensor modality. Typically, datasets consist of a series of point clouds with one type of sensor and a limited range of viewpoints. Therefore, when training a dataset with a particular signature, it is challenging to infer scenes outside of the original range of the viewpoints from the training dataset. This work develops a generative network that can predict the area in which an occlusion occurs and furnish the missing points. The output is a complete point cloud that is a more general representation and agnostic to the original viewpoint. We can then use the resulting point cloud as an input for a secondary method such as semantic or instance segmentation. We propose a learned sampling technique that uses the features to inform the point sampling instead of relying strictly on spatial information. We also introduce a new network structure that considers multiple point locations and augmentations to generate parallel features. The network is tested against other methods using our aerial occlusion dataset, DALES Viewpoints Version 2, and also against other point cloud completion networks on the Point Cloud Network (PCN) dataset. We show that it reduces occlusions visually and outperforms state-of-the-art point cloud completion networks in both Chamfers and Earth Mover’s Distance (EMD) metrics. We also show that using our occlusion reduction method as a pre-processing step improves semantic segmentation results compared to the same scenes processed without using our method.

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Xie, Zhexin, Peidong Liang, Jin Tao, Liang Zeng, Ziyang Zhao, Xiang Cheng, Jianhuan Zhang, and Chentao Zhang. "An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots." Electronics 11, no. 10 (May 18, 2022): 1612. http://dx.doi.org/10.3390/electronics11101612.

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Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.

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Zong, Chengjie, and Zhibo Wan. "CONTAINER SHIP CELL GUIDE ACCURACY CHECK TECHNOLOGY BASED ON IMPROVED 3D POINT CLOUD INSTANCE SEGMENTATION." Brodogradnja 73, no. 1 (January 1, 2022): 23–35. http://dx.doi.org/10.21278/brod73102.

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Generally, cell guides are installed in the cargo hold of container ships, which improve the loading and unloading efficiency of containers and fix containers when the ship is sailing. However, in actual production, due to the low accuracy of ship loading in sections, and the deviation of welding shrinkage and expansion in relevant sections, errors occur in the loading process of containers, resulting in hidden safety risks or significant economic losses. Given the above situation, it is particularly important to find a high-efficiency cell guide accuracy inspection method for construction monitoring. 3D scanner to obtain three-dimensional data is presented in this paper, based on this paper proposes a new method, this method will be used based on improved instances of 3 d point cloud segmentation model to cell guide the segmentation, and fitting container ship cell guide structure, and then realize the function of container simulation test box, cell guide after the segmentation precision inspection at the same time, for the practicality review, we compared the accuracy data gained from inspection simulation and the measured data. As a result, it was confirmed that both values were within about ±1.5mm. The validity, and reliability of the method are further verified.

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Wang, Di. "Unsupervised semantic and instance segmentation of forest point clouds." ISPRS Journal of Photogrammetry and Remote Sensing 165 (July 2020): 86–97. http://dx.doi.org/10.1016/j.isprsjprs.2020.04.020.

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Wang, Weiqi, Xiong You, Jian Yang, Mingzhan Su, Lantian Zhang, Zhenkai Yang, and Yingcai Kuang. "LiDAR-Based Real-Time Panoptic Segmentation via Spatiotemporal Sequential Data Fusion." Remote Sensing 14, no. 8 (April 7, 2022): 1775. http://dx.doi.org/10.3390/rs14081775.

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Fast and accurate semantic scene understanding is essential for mobile robots to operate in complex environments. An emerging research topic, panoptic segmentation, serves such a purpose by performing the tasks of semantic segmentation and instance segmentation in a unified framework. To improve the performance of LiDAR-based real-time panoptic segmentation, this study proposes a spatiotemporal sequential data fusion strategy that fused points in “thing classes” based on accurate data statistics. The data fusion strategy could increase the proportion of valuable data in unbalanced datasets, and thus managed to mitigate the adverse impact of class imbalance in the limited training data. Subsequently, by improving the codec network, the multiscale features shared by semantic and instance branches were efficiently aggregated to achieve accurate panoptic segmentation for each LiDAR scan. Experiments on the publicly available dataset SemanticKITTI showed that our approach could achieve an effective balance between accuracy and efficiency, and it was also applicable to other point cloud segmentation tasks.

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Petschnigg, Christina, and Jürgen Pilz. "Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning." Modelling 2, no. 1 (January 4, 2021): 1–17. http://dx.doi.org/10.3390/modelling2010001.

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The digital factory provides undoubtedly great potential for future production systems in terms of efficiency and effectivity. A key aspect on the way to realize the digital copy of a real factory is the understanding of complex indoor environments on the basis of three-dimensional (3D) data. In order to generate an accurate factory model including the major components, i.e., building parts, product assets, and process details, the 3D data that are collected during digitalization can be processed with advanced methods of deep learning. For instance, the semantic segmentation of a point cloud enables the identification of relevant objects within the environment. In this work, we propose a fully Bayesian and an approximate Bayesian neural network for point cloud segmentation. Both of the networks are used within a workflow in order to generate an environment model on the basis of raw point clouds. The Bayesian and approximate Bayesian networks allow us to analyse how different ways of estimating uncertainty in these networks improve segmentation results on raw point clouds. We achieve superior model performance for both, the Bayesian and the approximate Bayesian model compared to the frequentist one. This performance difference becomes even more striking when incorporating the networks’ uncertainty in their predictions. For evaluation, we use the scientific data set S3DIS as well as a data set, which was collected by the authors at a German automotive production plant. The methods proposed in this work lead to more accurate segmentation results and the incorporation of uncertainty information also makes this approach especially applicable to safety critical applications aside from our factory planning use case.

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Saovana, Natthapol, Nobuyoshi Yabuki, and Tomohiro Fukuda. "Automated point cloud classification using an image-based instance segmentation for structure from motion." Automation in Construction 129 (September 2021): 103804. http://dx.doi.org/10.1016/j.autcon.2021.103804.

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Liang, Zhidong, Ming Yang, Hao Li, and Chunxiang Wang. "3D Instance Embedding Learning With a Structure-Aware Loss Function for Point Cloud Segmentation." IEEE Robotics and Automation Letters 5, no. 3 (July 2020): 4915–22. http://dx.doi.org/10.1109/lra.2020.3004802.

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HAO, W., H. WANG, W. LIANG, M. ZHAO, and Z. XIAO. "Attention-Based Joint Semantic-Instance Segmentation of 3D Point Clouds." Advances in Electrical and Computer Engineering 22, no. 2 (2022): 19–28. http://dx.doi.org/10.4316/aece.2022.02003.

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Yang, Wangshan, Xinyi Liu, Yongjun Zhang, Yi Wan, and Zheng Ji. "Object-based building instance segmentation from airborne LiDAR point clouds." International Journal of Remote Sensing 43, no. 18 (September 17, 2022): 6783–808. http://dx.doi.org/10.1080/01431161.2022.2145582.

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Pellis, E., A. Murtiyoso, A. Masiero, G. Tucci, M. Betti, and P. Grussenmeyer. "AN IMAGE-BASED DEEP LEARNING WORKFLOW FOR 3D HERITAGE POINT CLOUD SEMANTIC SEGMENTATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-2/W1-2022 (February 25, 2022): 429–34. http://dx.doi.org/10.5194/isprs-archives-xlvi-2-w1-2022-429-2022.

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Abstract. The interest in high-resolution semantic 3D models of historical buildings continuously increased during the last decade, thanks to their utility in protection, conservation and restoration of cultural heritage sites. The current generation of surveying tools allows the quick collection of large and detailed amount of data: such data ensure accurate spatial representations of the buildings, but their employment in the creation of informative semantic 3D models is still a challenging task, and it currently still requires manual time-consuming intervention by expert operators. Hence, increasing the level of automation, for instance developing an automatic semantic segmentation procedure enabling machine scene understanding and comprehension, can represent a dramatic improvement in the overall processing procedure. In accordance with this observation, this paper aims at presenting a new workflow for the automatic semantic segmentation of 3D point clouds based on a multi-view approach. Two steps compose this workflow: first, neural network-based semantic segmentation is performed on building images. Then, image labelling is back-projected, through the use of masked images, on the 3D space by exploiting photogrammetry and dense image matching principles. The obtained results are quite promising, with a good performance in the image segmentation, and a remarkable potential in the 3D reconstruction procedure.

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Ning, Xiaojuan, Yishu Ma, Yuanyuan Hou, Zhiyong Lv, Haiyan Jin, and Yinghui Wang. "Semantic Segmentation Guided Coarse-to-Fine Detection of Individual Trees from MLS Point Clouds Based on Treetop Points Extraction and Radius Expansion." Remote Sensing 14, no. 19 (October 1, 2022): 4926. http://dx.doi.org/10.3390/rs14194926.

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Urban trees are vital elements of outdoor scenes via mobile laser scanning (MLS), accurate individual trees detection from disordered, discrete, and high-density MLS is an important basis for the subsequent analysis of city management and planning. However, trees cannot be easily extracted because of the occlusion with other objects in urban scenes. In this work, we propose a coarse-to-fine individual trees detection method from MLS point cloud data (PCD) based on treetop points extraction and radius expansion. Firstly, an improved semantic segmentation deep network based on PointNet is applied to segment tree points from the scanned urban scene, which combining spatial features and dimensional features. Next, through calculating the local maximum, the candidate treetop points are located. In addition, the optimized treetop points are extracted after the tree point projection plane was filtered to locate the candidate treetop points, and a distance rule is used to eliminate the pseudo treetop points then the optimized treetop points are obtained. Finally, after the initial clustering of treetop points and vertical layering of tree points, a top-down layer-by-layer segmentation based on radius expansion to realize the complete individual extraction of trees. The effectiveness of the proposed method is tested and evaluated on five street scenes in point clouds from Oakland outdoor MLS dataset. Furthermore, the proposed method is compared with two existing individual trees segmentation methods. Overall, the precision, recall, and F-score of instance segmentation are 98.33%, 98.33%, and 98.33%, respectively. The results indicate that our method can extract individual trees effectively and robustly in different complex environments.

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Schunck, David, Federico Magistri, Radu Alexandru Rosu, André Cornelißen, Nived Chebrolu, Stefan Paulus, Jens Léon, et al. "Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis." PLOS ONE 16, no. 8 (August 18, 2021): e0256340. http://dx.doi.org/10.1371/journal.pone.0256340.

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Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.

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Chi, Jinxin, Hao Wu, and Guohui Tian. "Object-Oriented 3D Semantic Mapping Based on Instance Segmentation." Journal of Advanced Computational Intelligence and Intelligent Informatics 23, no. 4 (July 20, 2019): 695–704. http://dx.doi.org/10.20965/jaciii.2019.p0695.

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Service robots gain both geometric and semantic information about the environment with the help of semantic mapping, providing more intelligent services. However, a majority of studies for semantic mapping thus far require priori knowledge 3D object models or maps with a few object categories that neglect separate individual objects. In view of these problems, an object-oriented 3D semantic mapping method is proposed by combining state-of-the-art deep-learning-based instance segmentation and a visual simultaneous localization and mapping (SLAM) algorithm, which helps robots not only gain navigation-oriented geometric information about the surrounding environment, but also obtain individually-oriented attribute and location information about the objects. Meanwhile, an object recognition and target association algorithm applied to continuous image frames is proposed by combining visual SLAM, which uses visual consistency between image frames to promote the result of object matching and recognition over continuous image frames, and improve the object recognition accuracy. Finally, a 3D semantic mapping system is implemented based on Mask R-CNN and ORB-SLAM2 frameworks. A simulation experiment is carried out on the ICL-NUIM dataset and the experimental results show that the system can generally recognize all the types of objects in the scene and generate fine point cloud models of these objects, which verifies the effectiveness of our algorithm.

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Bassier, Maarten, Meisam Yousefzadeh, and Maarten Vergauwen. "Comparison of 2D and 3D wall reconstruction algorithms from point cloud data for as-built BIM." Journal of Information Technology in Construction 25 (March 2, 2020): 173–92. http://dx.doi.org/10.36680/j.itcon.2020.011.

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As-built Building Information Models (BIMs) are becoming increasingly popular in the Architectural, Engineering, Construction, Owner and Operator (AECOO) industry. These models reflect the state of the building up to as-built conditions. The production of these models for existing buildings with no prior BIM includes the segmentation and classification of point cloud data and the reconstruction of the BIM objects. The automation of this process is a must since the manual Scan-to-BIM procedure is both time-consuming and error prone. However, the automated reconstruction from point cloud data is still ongoing research with both 2D and 3D approaches being proposed. There currently is a gap in the literature concerning the quality assessment of the created entities. In this research, we present the empirical comparison of both strategies with respect to existing specifications. A 3D and a 2D reconstruction method are implemented and tested on a real life test case. The experiments focus on the reconstruction of the wall geometry from unstructured point clouds as it forms the basis of the model. Both presented approaches are unsupervised methods that segment, classify and create generic wall elements. The first method operates on the 3D point cloud itself and consists of a general approach for the segmentation and classification and a class-specific reconstruction algorithm for the wall geometry. The point cloud is first segmented into planar clusters, after which a Random Forests classifier is used with geometric and contextual features for the semantic labelling. The final wall geometry is created based on the 3D point clusters representing the walls. The second method is an efficient Manhattan-world scene reconstruction algorithm that simultaneously segments and classifies the point cloud based on point feature histograms. The wall reconstruction is considered an instance of image segmentation by representing the data as 2D raster images. Both methods have promising results towards the reconstruction of wall geometry of multi-story buildings. The experiments report that over 80% of the walls were correctly segmented by both methods. Furthermore, the reconstructed geometry is conform Level-of-Accuracy 20 for 88% of the data by the first method and for 55% by the second method despite the Manhattan-world scene assumption. The empirical comparison showcases the fundamental differences in both strategies and will support the further development of these methods.

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Zhu, Jingwei, Joachim Gehrung, Rong Huang, Björn Borgmann, Zhenghao Sun, Ludwig Hoegner, Marcus Hebel, Yusheng Xu, and Uwe Stilla. "TUM-MLS-2016: An Annotated Mobile LiDAR Dataset of the TUM City Campus for Semantic Point Cloud Interpretation in Urban Areas." Remote Sensing 12, no. 11 (June 9, 2020): 1875. http://dx.doi.org/10.3390/rs12111875.

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In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation.

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Deschaud, Jean-Emmanuel, David Duque, Jean Pierre Richa, Santiago Velasco-Forero, Beatriz Marcotegui, and François Goulette. "Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping." Remote Sensing 13, no. 22 (November 21, 2021): 4713. http://dx.doi.org/10.3390/rs13224713.

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Paris-CARLA-3D is a dataset of several dense colored point clouds of outdoor environments built by a mobile LiDAR and camera system. The data are composed of two sets with synthetic data from the open source CARLA simulator (700 million points) and real data acquired in the city of Paris (60 million points), hence the name Paris-CARLA-3D. One of the advantages of this dataset is to have simulated the same LiDAR and camera platform in the open source CARLA simulator as the one used to produce the real data. In addition, manual annotation of the classes using the semantic tags of CARLA was performed on the real data, allowing the testing of transfer methods from the synthetic to the real data. The objective of this dataset is to provide a challenging dataset to evaluate and improve methods on difficult vision tasks for the 3D mapping of outdoor environments: semantic segmentation, instance segmentation, and scene completion. For each task, we describe the evaluation protocol as well as the experiments carried out to establish a baseline.

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Jiao, Yujun, and Zhishuai Yin. "A Two-Phase Cross-Modality Fusion Network for Robust 3D Object Detection." Sensors 20, no. 21 (October 23, 2020): 6043. http://dx.doi.org/10.3390/s20216043.

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A two-phase cross-modality fusion detector is proposed in this study for robust and high-precision 3D object detection with RGB images and LiDAR point clouds. First, a two-stream fusion network is built into the framework of Faster RCNN to perform accurate and robust 2D detection. The visible stream takes the RGB images as inputs, while the intensity stream is fed with the intensity maps which are generated by projecting the reflection intensity of point clouds to the front view. A multi-layer feature-level fusion scheme is designed to merge multi-modal features across multiple layers in order to enhance the expressiveness and robustness of the produced features upon which region proposals are generated. Second, a decision-level fusion is implemented by projecting 2D proposals to the space of the point cloud to generate 3D frustums, on the basis of which the second-phase 3D detector is built to accomplish instance segmentation and 3D-box regression on the filtered point cloud. The results on the KITTI benchmark show that features extracted from RGB images and intensity maps complement each other, and our proposed detector achieves state-of-the-art performance on 3D object detection with a substantially lower running time as compared to available competitors.

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Bai, Ling, Yinguo Li, Ming Cen, and Fangchao Hu. "3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing." Remote Sensing 13, no. 16 (August 19, 2021): 3288. http://dx.doi.org/10.3390/rs13163288.

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Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.

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Xu, Yajun, Shogo Arai, Fuyuki Tokuda, and Kazuhiro Kosuge. "A Convolutional Neural Network for Point Cloud Instance Segmentation in Cluttered Scene Trained by Synthetic Data Without Color." IEEE Access 8 (2020): 70262–69. http://dx.doi.org/10.1109/access.2020.2978506.

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Zanjani, Farhad Ghazvinian, Arash Pourtaherian, Svitlana Zinger, David Anssari Moin, Frank Claessen, Teo Cherici, Sarah Parinussa, and Peter H. N. de With. "Mask-MCNet: Tooth instance segmentation in 3D point clouds of intra-oral scans." Neurocomputing 453 (September 2021): 286–98. http://dx.doi.org/10.1016/j.neucom.2020.06.145.

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Li, Wei, Sijing Xie, Weidong Min, Yifei Jiang, Cheng Wang, and Jonathan Li. "Spherical coordinate transformation-embedded deep network for primitive instance segmentation of point clouds." International Journal of Applied Earth Observation and Geoinformation 113 (September 2022): 102983. http://dx.doi.org/10.1016/j.jag.2022.102983.

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Li, Jingyu, Rongfen Zhang, Yuhong Liu, Zaiteng Zhang, Runze Fan, and Wenjiang Liu. "The Method of Static Semantic Map Construction Based on Instance Segmentation and Dynamic Point Elimination." Electronics 10, no. 16 (August 5, 2021): 1883. http://dx.doi.org/10.3390/electronics10161883.

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Semantic information usually contains a description of the environment content, which enables mobile robot to understand the environment and improves its ability to interact with the environment. In high-level human–computer interaction application, the Simultaneous Localization and Mapping (SLAM) system not only needs higher accuracy and robustness, but also has the ability to construct a static semantic map of the environment. However, traditional visual SLAM lacks semantic information. Furthermore, in an actual scene, dynamic objects will reduce the system performance and also generate redundancy when constructing map. these all directly affect the robot’s ability to perceive and understand the surrounding environment. Based on ORB-SLAM3, this article proposes a new Algorithm that uses semantic information and the global dense optical flow as constraints to generate dynamic-static mask and eliminate dynamic objects. then, to further construct a static 3D semantic map under indoor dynamic environments, a fusion of 2D semantic information and 3D point cloud is carried out. the experimental results on different types of dataset sequences show that, compared with original ORB-SLAM3, both Absolute Pose Error (APE) and Relative Pose Error (RPE) have been ameliorated to varying degrees, especially on freiburg3-walking-xyz, the APE reduced by 97.78% from the original average value of 0.523, and RPE reduced by 52.33% from the original average value of 0.0193. Compared with DS-SLAM and DynaSLAM, our system improves real-time performance while ensuring accuracy and robustness. Meanwhile, the expected map with environmental semantic information is built, and the map redundancy caused by dynamic objects is successfully reduced. the test results in real scenes further demonstrate the effect of constructing static semantic maps and prove the effectiveness of our Algorithm.

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De Geyter, Sam, Jelle Vermandere, Heinder De Winter, Maarten Bassier, and Maarten Vergauwen. "Point Cloud Validation: On the Impact of Laser Scanning Technologies on the Semantic Segmentation for BIM Modeling and Evaluation." Remote Sensing 14, no. 3 (January 26, 2022): 582. http://dx.doi.org/10.3390/rs14030582.

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Building Information models created from laser scanning inputs are becoming increasingly commonplace, but the automation of the modeling and evaluation is still a subject of ongoing research. Current advancements mainly target the data interpretation steps, i.e., the instance and semantic segmentation by developing advanced deep learning models. However, these steps are highly influenced by the characteristics of the laser scanning technologies themselves, which also impact the reconstruction/evaluation potential. In this work, the impact of different data acquisition techniques and technologies on these procedures is studied. More specifically, we quantify the capacity of static, trolley, backpack, and head-worn mapping solutions and their semantic segmentation results such as for BIM modeling and analyses procedures. For the analysis, international standards and specifications are used wherever possible. From the experiments, the suitability of each platform is established, along with the pros and cons of each system. Overall, this work provides a much needed update on point cloud validation that is needed to further fuel BIM automation.

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Xu, Shuangjie, Rui Wan, Maosheng Ye, Xiaoyi Zou, and Tongyi Cao. "Sparse Cross-Scale Attention Network for Efficient LiDAR Panoptic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2920–28. http://dx.doi.org/10.1609/aaai.v36i3.20197.

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Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and that objects are too close to separate each other. Recent literature addresses these problems by time-consuming grouping processes such as dual-clustering, mean-shift offsets and etc., or by bird-eye-view (BEV) dense centroid representation that downplays geometry. However, the long-range geometry relationship has not been sufficiently modeled by local feature learning from the above methods. To this end, we present SCAN, a novel sparse cross-scale attention network to first align multi-scale sparse features with global voxel-encoded attention to capture the long-range relationship of instance context, which is able to boost the regression accuracy of the over-segmented large objects. For the surface-aggregated points, SCAN adopts a novel sparse class-agnostic representation of instance centroids, which can not only maintain the sparsity of aligned features to solve the under-segmentation on small objects, but also reduce the computation amount of the network through sparse convolution. Our method outperforms previous methods by a large margin in the SemanticKITTI dataset for the challenging 3D PS task, achieving 1st place with a real-time inference speed.

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Murtiyoso, Arnadi, Mirza Veriandi, Deni Suwardhi, Budhy Soeksmantono, and Agung Budi Harto. "Automatic Workflow for Roof Extraction and Generation of 3D CityGML Models from Low-Cost UAV Image-Derived Point Clouds." ISPRS International Journal of Geo-Information 9, no. 12 (December 12, 2020): 743. http://dx.doi.org/10.3390/ijgi9120743.

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Developments in UAV sensors and platforms in recent decades have stimulated an upsurge in its application for 3D mapping. The relatively low-cost nature of UAVs combined with the use of revolutionary photogrammetric algorithms, such as dense image matching, has made it a strong competitor to aerial lidar mapping. However, in the context of 3D city mapping, further 3D modeling is required to generate 3D city models which is often performed manually using, e.g., photogrammetric stereoplotting. The aim of the paper was to try to implement an algorithmic approach to building point cloud segmentation, from which an automated workflow for the generation of roof planes will also be presented. 3D models of buildings are then created using the roofs’ planes as a base, therefore satisfying the requirements for a Level of Detail (LoD) 2 in the CityGML paradigm. Consequently, the paper attempts to create an automated workflow starting from UAV-derived point clouds to LoD 2-compatible 3D model. Results show that the rule-based segmentation approach presented in this paper works well with the additional advantage of instance segmentation and automatic semantic attribute annotation, while the 3D modeling algorithm performs well for low to medium complexity roofs. The proposed workflow can therefore be implemented for simple roofs with a relatively low number of planar surfaces. Furthermore, the automated approach to the 3D modeling process also helps to maintain the geometric requirements of CityGML such as 3D polygon coplanarity vis-à-vis manual stereoplotting.

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Zhang, Hongyan, Huawei Liang, Tao Ni, Lingtao Huang, and Jinsong Yang. "Research on Multi-Object Sorting System Based on Deep Learning." Sensors 21, no. 18 (September 17, 2021): 6238. http://dx.doi.org/10.3390/s21186238.

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As a complex task, robot sorting has become a research hotspot. In order to enable robots to perform simple, efficient, stable and accurate sorting operations for stacked multi-objects in unstructured scenes, a robot multi-object sorting system is built in this paper. Firstly, the training model of rotating target detection is constructed, and the placement state of five common objects in unstructured scenes is collected as the training set for training. The trained model is used to obtain the position, rotation angle and category of the target object. Then, the instance segmentation model is constructed, and the same data set is made, and the instance segmentation network model is trained. Then, the optimized Mask R-CNN instance segmentation network is used to segment the object surface pixels, and the upper surface point cloud is extracted to calculate the normal vector. Then, the angle obtained by the normal vector of the upper surface and the rotation target detection network is fused with the normal vector to obtain the attitude of the object. At the same time, the grasping order is calculated according to the average depth of the surface. Finally, after the obtained object posture, category and grasping sequence are fused, the performance of the rotating target detection network, the instance segmentation network and the robot sorting system are tested on the established experimental platform. Based on this system, this paper carried out an experiment on the success rate of object capture in a single network and an integrated network. The experimental results show that the multi-object sorting system based on deep learning proposed in this paper can sort stacked objects efficiently, accurately and stably in unstructured scenes.

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Liu, Shuang, Haili Sun, Zhenxin Zhang, Yuqi Li, Ruofei Zhong, Jincheng Li, and Siyun Chen. "A Multiscale Deep Feature for the Instance Segmentation of Water Leakages in Tunnel Using MLS Point Cloud Intensity Images." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–16. http://dx.doi.org/10.1109/tgrs.2022.3158660.

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Wang, Yongjun, Tengping Jiang, Jing Liu, Xiaorui Li, and Chong Liang. "Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds." ISPRS International Journal of Geo-Information 9, no. 10 (October 10, 2020): 595. http://dx.doi.org/10.3390/ijgi9100595.

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Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree.

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Journal articles on the topic 'Point cloud instance segmentation'

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