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Добірка наукової літератури з теми "3D non-rigid shapes"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "3D non-rigid shapes"

Yang, Jingyu, Ke Li, Kun Li, and Yu-Kun Lai. "Sparse Non-rigid Registration of 3D Shapes." Computer Graphics Forum 34, no. 5 (August 2015): 89–99. http://dx.doi.org/10.1111/cgf.12699.

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Lladó, Xavier, Alessio Del Bue, Arnau Oliver, Joaquim Salvi, and Lourdes Agapito. "Reconstruction of non-rigid 3D shapes from stereo-motion." Pattern Recognition Letters 32, no. 7 (May 2011): 1020–28. http://dx.doi.org/10.1016/j.patrec.2011.02.010.

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Kuang, Zhenzhong, Zongmin Li, Xiaxia Jiang, Yujie Liu, and Hua Li. "Retrieval of non-rigid 3D shapes from multiple aspects." Computer-Aided Design 58 (January 2015): 13–23. http://dx.doi.org/10.1016/j.cad.2014.08.004.

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Liu, Bin, Weiming Wang, Jun Zhou, Bo Li, and Xiuping Liu. "Detail-Preserving Shape Unfolding." Sensors 21, no. 4 (February 8, 2021): 1187. http://dx.doi.org/10.3390/s21041187.

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Анотація:

Canonical extrinsic representations for non-rigid shapes with different poses are preferable in many computer graphics applications, such as shape correspondence and retrieval. The main reason for this is that they give a pose invariant signature for those jobs, which significantly decreases the difficulty caused by various poses. Existing methods based on multidimentional scaling (MDS) always result in significant geometric distortions. In this paper, we present a novel shape unfolding algorithm, which deforms any given 3D shape into a canonical pose that is invariant to non-rigid transformations. The proposed method can effectively preserve the local structure of a given 3D model with the regularization of local rigid transform energy based on the shape deformation technique, and largely reduce geometric distortion. Our algorithm is quite simple and only needs to solve two linear systems during alternate iteration processes. The computational efficiency of our method can be improved with parallel computation and the robustness is guaranteed with a cascade strategy. Experimental results demonstrate the enhanced efficacy of our algorithm compared with the state-of-the-art methods on 3D shape unfolding.

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Agudo, Antonio, Francesc Moreno-Noguer, Begoña Calvo, and J. M. M. Montiel. "Real-time 3D reconstruction of non-rigid shapes with a single moving camera." Computer Vision and Image Understanding 153 (December 2016): 37–54. http://dx.doi.org/10.1016/j.cviu.2016.05.004.

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Kuang, Zhenzhong, Zongmin Li, Xiaxia Jiang, and Yujie Liu. "Exploration in improving retrieval quality and robustness for deformable non-rigid 3D shapes." Multimedia Tools and Applications 74, no. 23 (August 14, 2014): 10335–66. http://dx.doi.org/10.1007/s11042-014-2170-4.

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Hu, Xiaobo, Dejun Zhang, Jinzhi Chen, Yiqi Wu, and Yilin Chen. "NrtNet: An Unsupervised Method for 3D Non-Rigid Point Cloud Registration Based on Transformer." Sensors 22, no. 14 (July 8, 2022): 5128. http://dx.doi.org/10.3390/s22145128.

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Self-attention networks have revolutionized the field of natural language processing and have also made impressive progress in image analysis tasks. Corrnet3D proposes the idea of first obtaining the point cloud correspondence in point cloud registration. Inspired by these successes, we propose an unsupervised network for non-rigid point cloud registration, namely NrtNet, which is the first network using a transformer for unsupervised large deformation non-rigid point cloud registration. Specifically, NrtNet consists of a feature extraction module, a correspondence matrix generation module, and a reconstruction module. Feeding a pair of point clouds, our model first learns the point-by-point features and feeds them to the transformer-based correspondence matrix generation module, which utilizes the transformer to learn the correspondence probability between pairs of point sets, and then the correspondence probability matrix conducts normalization to obtain the correct point set corresponding matrix. We then permute the point clouds and learn the relative drift of the point pairs to reconstruct the point clouds for registration. Extensive experiments on synthetic and real datasets of non-rigid 3D shapes show that NrtNet outperforms state-of-the-art methods, including methods that use grids as input and methods that directly compute point drift.

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Shen, Jiayan, Shutong Du, Ziyao Xu, Tiansheng Gan, Stephan Handschuh-Wang, and Xueli Zhang. "Anti-Freezing, Non-Drying, Localized Stiffening, and Shape-Morphing Organohydrogels." Gels 8, no. 6 (May 25, 2022): 331. http://dx.doi.org/10.3390/gels8060331.

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Artificial shape-morphing hydrogels are emerging toward various applications, spanning from electronic skins to healthcare. However, the low freezing and drying tolerance of hydrogels hinder their practical applications in challenging environments, such as subzero temperatures and arid conditions. Herein, we report on a shape-morphing system of tough organohydrogels enabled by the spatially encoded rigid structures and its applications in conformal packaging of “island–bridge” stretchable electronics. To validate this method, programmable shape morphing of Fe (III) ion-stiffened Ca-alginate/polyacrylamide (PAAm) tough organohydrogels down to −50 °C, with long-term preservation of their 3D shapes at arid or even vacuum conditions, was successfully demonstrated, respectively. To further illustrate the potency of this approach, the as-made organohydrogels were employed as a material for the conformal packaging of non-stretchable rigid electronic components and highly stretchable liquid metal (galinstan) conductors, forming a so-called “island–bridge” stretchable circuit. The conformal packaging well addresses the mechanical mismatch between components with different elastic moduli. As such, the as-made stretchable shape-morphing device exhibits a remarkably high mechanical durability that can withstand strains as high as 1000% and possesses long-term stability required for applications under challenging conditions.

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Raju, Ashwin, Shun Miao, Dakai Jin, Le Lu, Junzhou Huang, and Adam P. Harrison. "Deep Implicit Statistical Shape Models for 3D Medical Image Delineation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2135–43. http://dx.doi.org/10.1609/aaai.v36i2.20110.

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3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models (SSMs) that imposed anatomical constraints and produced high quality surfaces were a core technology. Today’s fully-convolutional networks (FCNs), while dominant, do not offer these capabilities. We present deep implicit statistical shape models (DISSMs), a new approach that marries the representation power of deep networks with the benefits of SSMs. DISSMs use an implicit representation to produce compact and descriptive deep surface embeddings that permit statistical models of anatomical variance. To reliably fit anatomically plausible shapes to an image, we introduce a novel rigid and non-rigid pose estimation pipeline that is modelled as a Markov decision process (MDP). Intra-dataset experiments on the task of pathological liver segmentation demonstrate that DISSMs can perform more robustly than four leading FCN models, including nnU-Net + an adversarial prior: reducing the mean Hausdorff distance (HD) by 7.5-14.3 mm and improving the worst case Dice-Sørensen coefficient (DSC) by 1.2-2.3%. More critically, cross-dataset experiments on an external and highly challenging clinical dataset demonstrate that DISSMs improve the mean DSC and HD by 2.1-5.9% and 9.9-24.5 mm, respectively, and the worst-case DSC by 5.4-7.3%. Supplemental validation on a highly challenging and low-contrast larynx dataset further demonstrate DISSM’s improvements. These improvements are over and above any benefits from representing delineations with high-quality surfaces.

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Zhu, Mengru, and Jong Han Lee. "Deep Learning-Based 3D Shape Feature Extraction on Flash Animation Style." Wireless Communications and Mobile Computing 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/7999312.

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Flash animation, as a kind of digital learning resource, is an important media for delivering information content, and more importantly, it is an important online learning resource with text, graphics, images, audio, video, interaction, dynamic effects, etc. Flash animation, with its powerful multimedia interaction and presentation capabilities, is widely used in distance education, high-quality course websites, Q&A platforms, etc. With the continuous development of deep learning, the 3D shape feature extraction method combined with deep learning has become a hot research topic. In this paper, we combine deep learning with traditional 3D shape feature extraction methods, so that we can not only break the bottleneck of nondeep learning methods but also improve the accuracy of 3D shape data classification and retrieval tasks, especially in the case of non-rigid 3D shapes. The scheme in this paper not only does not require a large number of training samples but also its feature extraction for flash animation is accurate. Experiments show that the success rate of accurate feature extraction of this paper’s scheme is higher than that of the state-of-the-art methods.

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Більше джерел

Дисертації з теми "3D non-rigid shapes"

Limberger, Frederico Artur. "Spectral signatures for non-rigid 3D shape retrieval." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/18036/.

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This thesis addresses problems associated with computing spectral shape signatures for non-rigid 3D object retrieval. More specifically, we use spectral shape analysis tools to describe the characteristics of different 3D object representations. This thesis tries to answer whether spectral shape analysis tools can enhance classical shape signatures to improve the performance of the non-rigid shape retrieval problem. Furthermore, it describes the stages of the framework for composing non-rigid shape signatures, built from the shape Laplacian. This thesis presents four methods to improve each part of the framework for computing spectral shape signatures. The first stage comprises computing the right shape spectrum to describe 3D objects. We introduce the Kinetic Laplace-Beltrami operator which computes enhanced spectral components from 3D meshes specific to non-rigid shape retrieval and we also introduce the Mesh-Free Laplace Operator which computes more precise and robust spectral components from 3D point clouds. After computing the shape spectrum, we propose the Improved Wave Kernel Signature, a more discriminative local descriptor built from the Laplacian eigenfunctions. This descriptor is used throughout this thesis and it achieves, in most cases, state-of-the-art performances. Then, we define a new framework for encoding sparse local descriptors into shape signatures that can be compared to each other. Here, we show how to use the Fisher Vector and Super Vector to encode spectral descriptors and also how to compute dissimilarities between shape signatures using the Efficient Manifold Ranking. Furthermore, we describe the construction of the Point-Cloud Shape Retrieval of Non-Rigid Toys dataset, aimed in testing non-rigid shape signatures on point clouds, after we evidenced a lack of point-cloud benchmarks in the literature. With these ingredients, we are able to construct shape signatures which are specially built for non-rigid shape retrieval.

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Tao, Lili. "3D non-rigid reconstruction with prior shape constraints." Thesis, University of Central Lancashire, 2014. http://clok.uclan.ac.uk/10717/.

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3D non-rigid shape recovery from a single uncalibrated camera is a challenging, under-constrained problem in computer vision. Although tremendous progress has been achieved towards solving the problem, two main limitations still exist in most previous solutions. First, current methods focus on non-incremental solutions, that is, the algorithms require collection of all the measurement data before the reconstruction takes place. This methodology is inherently unsuitable for applications requiring real-time solutions. At the same time, most of the existing approaches assume that 3D shapes can be accurately modelled in a linear subspace. These methods are simple and have been proven effective for reconstructions of objects with relatively small deformations, but have considerable limitations when the deformations are large or complex. The non-linear deformations are often observed in highly flexible objects for which the use of the linear model is impractical. Note that specific types of shape variation might be governed by only a small number of parameters and therefore can be well-represented in a low dimensional manifold. The methods proposed in this thesis aim to estimate the non-rigid shapes and the corresponding camera trajectories, based on both the observations and the prior learned manifold. Firstly, an incremental approach is proposed for estimating the deformable objects. An important advantage of this method is the ability to reconstruct the 3D shape from a newly observed image and update the parameters in 3D shape space. However, this recursive method assumes the deformable shapes only have small variations from a mean shape, thus is still not feasible for objects subject to large scale deformations. To address this problem, a series of approaches are proposed, all based on non-linear manifold learning techniques. Such manifold is used as a shape prior, with the reconstructed shapes constrained to lie within the manifold. Those non-linear manifold based approaches significantly improve the quality of reconstructed results and are well-adapted to different types of shapes undergoing significant and complex deformations. Throughout the thesis, methods are validated quantitatively on 2D points sequences projected from the 3D motion capture data for a ground truth comparison, and are qualitatively demonstrated on real example of 2D video sequences. Comparisons are made for the proposed methods against several state-of-the-art techniques, with results shown for a variety of challenging deformable objects. Extensive experiments also demonstrate the robustness of the proposed algorithms with respect to measurement noise and missing data.

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Zhang, Chao. "Learning non-rigid, 3D shape variations using statistical, physical and geometric models." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22342/.

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3D shape modelling is a fundamental component in computer vision and computer graphics. Applications include shape interpolation and extrapolation, shape reconstruction, motion capture and mesh editing, etc. By "modelling" we mean the process of learning a parameter-driven model. This thesis focused on the scope of statistical modelling for 3D non-rigid shapes, such as human faces and bodies. The problem is challenging due to highly non-linear deformations, high dimensionality, and data sparsity. Several new algorithms are proposed for 3D shape modelling, 3D shape matching (computing dense correspondence) and applications. First, we propose a variant of Principal Component Analysis called "Shell PCA" which provides a physically-inspired statistical shape model. This is our first attempt to use a physically plausible metric (specifically, the discrete shell model) for statistical shape modelling. Second, we further develop this line of work into a fully Riemannian approach called "Shell PGA". We demonstrate how to perform Principal Geodesic Analysis in the space of discrete shells. To achieve this, we present an alternate formulation of PGA which avoids working in the tangent space and deals with shapes lying on the manifold directly. Unlike displacement-based methods, Shell PGA is invariant to rigid body motion, and therefore alignment preprocessing such as Procrustes analysis is not needed. Third, we propose a groupwise shape matching method using functional map representation. Targeting at near-isometric deformations, we consider groupwise optimisation of consistent functional maps over a product of Stiefel manifolds, and optimise over a minimal subset of the transformations for efficiency. Last, we show that our proposed shape model achieves state-of-the-art performance in two very challenging applications: handle-based mesh editing, and model fitting using motion capture data. We also contribute a new algorithm for human body shape estimation using clothed scan sequence, along with a new dataset "BUFF" for evaluation.

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Valencia, Angel. "3D Shape Deformation Measurement and Dynamic Representation for Non-Rigid Objects under Manipulation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40718.

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Dexterous robotic manipulation of non-rigid objects is a challenging problem but necessary to explore as robots are increasingly interacting with more complex environments in which such objects are frequently present. In particular, common manipulation tasks such as molding clay to a target shape or picking fruits and vegetables for use in the kitchen, require a high-level understanding of the scene and objects. Commonly, the behavior of non-rigid objects is described by a model. Although, well-established modeling techniques are difficult to apply in robotic tasks since objects and their properties are unknown in such unstructured environments. This work proposes a sensing and modeling framework to measure the 3D shape deformation of non-rigid objects. Unlike traditional methods, this framework explores data-driven learning techniques focused on shape representation and deformation dynamics prediction using a graph-based approach. The proposal is validated experimentally, analyzing the performance of the representation model to capture the current state of the non-rigid object shape. In addition, the performance of the prediction model is analyzed in terms of its ability to produce future states of the non-rigid object shape due to the manipulation actions of the robotic system. The results suggest that the representation model is able to produce graphs that closely capture the deformation behavior of the non-rigid object. Whereas, the prediction model produces visually plausible graphs when short-term predictions are required.

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Allain, Benjamin. "Suivi volumétrique de formes 3D non rigides." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAM017/document.

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Dans cette thèse nous proposons des algorithmes pour le suivi 3D du mouvement des objects déformables à partir de plusieurs caméras vidéo. Bien qu’une suite de reconstructions tridimensionnelles peut être obtenue par des méthodes de reconstruction statique, celle-ci ne représente pas le mouvement. Nous voulons produire une représentation temporellement cohérente de la suite de formes prises par l’object. Précisément, nous souhaitons représenter l’objet par une surface maillée 3D dont les sommets se déplacent au cours du temps mais dont la topologie reste identique.Contrairement à beaucoup d’approches existantes, nous proposons de représenter le mouvement du volume intérieur des formes, dans le but de mieux représenter la nature volumétrique des objets. Nous traitons de manière volumétrique les problèmes fondamentaux du suivi déformable que sont l’association d’éléments semblables entre deux formes et la modélisation de la déformation. En particulier, nous adaptons au formes volumétriques les modèles d’association EM-ICP non-rigide ansi que l’association par détection par apprentissage automatique.D’autre part, nous abordons la question de la modélisation de l’évolution temporelle de la déformation au cours d’une séquence dans le but de mieux contraindre le problème du suivi temporel. Pour cela, nous modélisons un espace de forme construit autour de propriétés de déformations locales que nous apprenons automatiqument lors du suivi.Nous validons nos algorithmes de suivi sur des séquences vidéo multi-caméras avec vérité terrain (silhouettes et suivi par marqueurs). Nos résultats se révèlent meilleurs ou équivalents à ceux obtenus avec les méthodes de l’état de l’art.Enfin, nous démontrons que le suivi volumétrique et la représentation que nous avons choisie permettent de produire des animations 3D qui combinent l’acquisition et la simulation de mouvement
In this thesis we propose algorithms for tracking 3D deformable shapes in motion from multiview video. Although series of reconstructed 3D shapes can be obtained by applying a static reconstruction algorithm to each temporal frame independently, such series do not represent motion. Instead, we want to provide a temporally coherent representation of the sequence of shapes resulting from temporal evolutions of a shape. Precisely, we want to represent the observed shape sequence as a 3D surface mesh whose vertices move in time but whose topology is constant.In contrast with most existing approaches, we propose to represent the motion of inner shape volumes, with the aim of better accounting for the volumetric nature of the observed object. We provide a fully volumetric approach to the fundamental problems of deformable shape tracking, which are the association between corresponding shape elements and the deformation model. In particular, we extend to a volumetric shape representation the EM-ICP tracking framework and the association-by-detection strategy.Furthermore, in order to better constrain the shape tracking problem, we propose a model for the temporal evolution of deformation. Our deformation model defines a shape space parametrized by variables that capture local deformation properties of the shape and whose values are automatically learned during the tracking process.We validate our tracking algorithms on several multiview video sequences with ground truth (silhouette and marker-based tracking). Our results are better or comparable to state of the art approaches.Finally, we show that volumetric tracking and the shape representation we choose can be leveraged for producing shape animations which combine captured and simulatated motion

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Gallardo, Mathias. "Contributions to Monocular Deformable 3D Reconstruction : Curvilinear Objects and Multiple Visual Cues." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC021/document.

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La reconstruction 3D monoculaire déformable est le problème général d'estimation de forme 3D d'un objet déformable à partir d'images 2D. Plusieurs scénarios ont émergé : le Shape-from-Template (SfT) et le Non-Rigid Structure-from-Motion (NRSfM) sont deux approches qui ont été grandement étudiées pour leur applicabilité. La première utilise une seule image qui montre un objet se déformant et un patron (une forme 3D texturée de l'objet dans une pose de référence). La seconde n'utilise pas de patron, mais utilise plusieurs images et estime la forme 3D dans chaque image. Les deux approches s'appuient sur le mouvement de points de correspondances entre les images et sur des a priori de déformations, restreignant ainsi leur utilisation à des surfaces texturées qui se déforment de manière lisse. Cette thèse fait avancer l'état de l'art du SfT et du NRSfM dans deux directions. La première est l'étude du SfT dans le cas de patrons 1D (c’est-à-dire des courbes comme des cordes et des câbles). La seconde direction est le développement d'algorithmes de SfT et de NRSfM qui exploitent plusieurs indices visuels et qui résolvent des cas réels et complexes non-résolus précédemment. Nous considérons des déformations isométriques et reconstruisons la partie extérieure de l'objet. Les contributions techniques et scientifiques de cette thèse sont divisées en quatre parties.La première partie de cette thèse étudie le SfT curvilinéaire, qui est le cas du patron curvilinéaire plongé dans un espace 2D ou 3D. Nous proposons une analyse théorique approfondie et des solutions pratiques pour le SfT curvilinéaire. Malgré son apparente simplicité, le SfT curvilinéaire s'est avéré être un problème complexe : il ne peut pas être résolu à l'aide de solutions locales non-holonomes d'une équation différentielle ordinaire et ne possède pas de solution unique, mais un nombre fini de solutions ambiguës. Une contribution technique majeure est un algorithme basé sur notre théorie, qui génère toutes les solutions ambiguës. La deuxième partie de cette thèse traite d'une limitation des méthodes de SfT : la reconstruction de plis. Cette limitation vient de la parcimonie de la contrainte de mouvement et de la régularisation. Nous proposons deux contributions qui s'appuient sur un cadre de minimisation d'énergie non-convexe. Tout d'abord, nous complétons la contrainte de mouvement avec une contrainte robuste de bord. Ensuite, nous modélisons implicitement les plis à l'aide d'une représentation dense de la surface basée maillage et d'une contrainte robuste de lissage qui désactive automatiquement le lissage de la courbure sans connaître a priori la position des plis.La troisième partie de cette thèse est dédiée à une autre limitation du SfT : la reconstruction de surfaces peu texturées. Cette limitation vient de la difficulté d'obtenir des correspondances (parcimonieuses ou denses) sur des surfaces peu texturées. Comme l'ombrage révèle les détails sur des surfaces peu texturées, nous proposons de combiner l'ombrage avec le SfT. Nous présentons deux contributions. La première est une initialisation en cascade qui estime séquentiellement la déformation de la surface, l'illumination de la scène, la réponse de la caméra et enfin les albédos de la surface à partir d'images monoculaires où la surface se déforme. La seconde est l'intégration de l'ombrage à notre précédent cadre de minimisation d'énergie afin de raffiner simultanément les paramètres photométriques et de déformation.La dernière partie de cette thèse relâche la connaissance du patron et aborde deux limitations du NRSfM : la reconstruction de surfaces peu texturées avec des plis. Une contribution majeure est l'extension du second cadre d'optimisation pour la reconstruction conjointe de la forme 3D de la surface sur toutes les images d'entrée et des albédos de la surface sans en connaître un patron
Monocular deformable 3D reconstruction is the general problem of recovering the 3D shape of a deformable object from monocular 2D images. Several scenarios have emerged: the Shape-from-Template (SfT) and the Non-Rigid Structure-from-Motion (NRSfM) are two approaches intensively studied for their practicability. The former uses a single image depicting the deforming object and a template (a textured 3D shape of this object in a reference pose). The latter does not use a template, but uses several images and recovers the 3D shape in each image. Both approaches rely on the motion of correspondences between the images and deformation priors, which restrict their use to well-textured surfaces which deform smoothly. This thesis advances the state-of-the-art in SfT and NRSfM in two main directions. The first direction is to study SfT for the case of 1D templates (i.e. curved, thin structures such as ropes and cables). The second direction is to develop algorithms in SfT and NRSfM that exploit multiple visual cues and can solve complex, real-world cases which were previously unsolved. We focus on isometric deformations and reconstruct the outer part of the object. The technical and scientific contributions of this thesis are divided into four parts. The first part of this thesis studies the case of a curvilinear template embedded in 2D or 3D space, referred to Curve SfT. We propose a thorough theoretical analysis and practical solutions for Curve SfT. Despite its apparent simplicity, Curve SfT appears to be a complex problem: it cannot be solved locally using exact non-holonomic partial differential equation and is only solvable up to a finite number of ambiguous solutions. A major technical contribution is a computational solution based on our theory, which generates all the ambiguous solutions.The second part of this thesis deals with a limitation of SfT methods: reconstructing creases. This is due to the sparsity of the motion constraint and regularization. We propose two contributions which rely on a non-convex energy minimization framework. First, we complement the motion constraint with a robust boundary contour constraint. Second, we implicitly model creases with a dense mesh-based surface representation and an associated robust smoothing constraint, which deactivates curvature smoothing automatically where needed, without knowing a priori the crease location. The third part of this thesis is dedicated to another limitation of SfT: reconstructing poorly-textured surfaces. This is due to correspondences which cannot be obtained so easily on poorly-textured surfaces (either sparse or dense). As shading reveals details on poorly-textured surfaces, we propose to combine shading and SfT. We have two contributions. The first is a cascaded initialization which estimates sequentially the surface's deformation, the scene illumination, the camera response and then the surface albedos from deformed monocular images. The second is to integrate shading to our previous energy minimization framework for simultaneously refining deformation and photometric parameters.The last part of this thesis relaxes the knowledge of the template and addresses two limitations of NRSfM: reconstructing poorly-textured surfaces with creases. Our major contribution is an extension of the second framework to recover jointly the 3D shapes of all input images and the surface albedos without any template

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Ye, Mao. "MONOCULAR POSE ESTIMATION AND SHAPE RECONSTRUCTION OF QUASI-ARTICULATED OBJECTS WITH CONSUMER DEPTH CAMERA." UKnowledge, 2014. http://uknowledge.uky.edu/cs_etds/25.

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Quasi-articulated objects, such as human beings, are among the most commonly seen objects in our daily lives. Extensive research have been dedicated to 3D shape reconstruction and motion analysis for this type of objects for decades. A major motivation is their wide applications, such as in entertainment, surveillance and health care. Most of existing studies relied on one or more regular video cameras. In recent years, commodity depth sensors have become more and more widely available. The geometric measurements delivered by the depth sensors provide significantly valuable information for these tasks. In this dissertation, we propose three algorithms for monocular pose estimation and shape reconstruction of quasi-articulated objects using a single commodity depth sensor. These three algorithms achieve shape reconstruction with increasing levels of granularity and personalization. We then further develop a method for highly detailed shape reconstruction based on our pose estimation techniques. Our first algorithm takes advantage of a motion database acquired with an active marker-based motion capture system. This method combines pose detection through nearest neighbor search with pose refinement via non-rigid point cloud registration. It is capable of accommodating different body sizes and achieves more than twice higher accuracy compared to a previous state of the art on a publicly available dataset. The above algorithm performs frame by frame estimation and therefore is less prone to tracking failure. Nonetheless, it does not guarantee temporal consistent of the both the skeletal structure and the shape and could be problematic for some applications. To address this problem, we develop a real-time model-based approach for quasi-articulated pose and 3D shape estimation based on Iterative Closest Point (ICP) principal with several novel constraints that are critical for monocular scenario. In this algorithm, we further propose a novel method for automatic body size estimation that enables its capability to accommodate different subjects. Due to the local search nature, the ICP-based method could be trapped to local minima in the case of some complex and fast motions. To address this issue, we explore the potential of using statistical model for soft point correspondences association. Towards this end, we propose a unified framework based on Gaussian Mixture Model for joint pose and shape estimation of quasi-articulated objects. This method achieves state-of-the-art performance on various publicly available datasets. Based on our pose estimation techniques, we then develop a novel framework that achieves highly detailed shape reconstruction by only requiring the user to move naturally in front of a single depth sensor. Our experiments demonstrate reconstructed shapes with rich geometric details for various subjects with different apparels. Last but not the least, we explore the applicability of our method on two real-world applications. First of all, we combine our ICP-base method with cloth simulation techniques for Virtual Try-on. Our system delivers the first promising 3D-based virtual clothing system. Secondly, we explore the possibility to extend our pose estimation algorithms to assist physical therapist to identify their patients’ movement dysfunctions that are related to injuries. Our preliminary experiments have demonstrated promising results by comparison with the gold standard active marker-based commercial system. Throughout the dissertation, we develop various state-of-the-art algorithms for pose estimation and shape reconstruction of quasi-articulated objects by leveraging the geometric information from depth sensors. We also demonstrate their great potentials for different real-world applications.

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Melzi, Simone. "Local Geometry Processing for Deformations of Non-Rigid 3D Shapes." Doctoral thesis, 2018. http://hdl.handle.net/11562/982676.

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Geometry processing and in particular spectral geometry processing deal with many different deformations that complicate shape analysis problems for non-rigid 3D objects. Furthermore, pointwise description of surfaces has increased relevance for several applications such as shape correspondences and matching, shape representation, shape modelling and many others. In this thesis we propose four local approaches to face the problems generated by the deformations of real objects and improving the pointwise characterization of surfaces. Differently from global approaches that work simultaneously on the entire shape we focus on the properties of each point and its local neighborhood. Global analysis of shapes is not negative in itself. However, having to deal with local variations, distortions and deformations, it is often challenging to relate two real objects globally. For this reason, in the last decades, several instruments have been introduced for the local analysis of images, graphs, shapes and surfaces. Starting from this idea of localized analysis, we propose both theoretical insights and application tools within the local geometry processing domain. In more detail, we extend the windowed Fourier transform from the standard Euclidean signal processing to different versions specifically designed for spectral geometry processing. Moreover, from the spectral geometry processing perspective, we define a new family of localized basis for the functional space defined on surfaces that improve the spatial localization for standard applications in this field. Finally, we introduce the discrete time evolution process as a framework that characterizes a point through its pairwise relationship with the other points on the surface in an increasing scale of locality. The main contribute of this thesis is a set of tools for local geometry processing and local spectral geometry processing that could be used in standard useful applications. The overall observation of our analysis is that localization around points could factually improve the geometry processing in many different applications.

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Частини книг з теми "3D non-rigid shapes"

Jribi, Majdi, and Faouzi Ghorbel. "A Novel Canonical Form for the Registration of Non Rigid 3D Shapes." In Computer Analysis of Images and Patterns, 230–41. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23117-4_20.

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Dai, Hang, Nick Pears, and William Smith. "Non-rigid 3D Shape Registration Using an Adaptive Template." In Lecture Notes in Computer Science, 48–63. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11018-5_5.

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Wang, Hanyu, Jianwei Guo, Dong-Ming Yan, Weize Quan, and Xiaopeng Zhang. "Learning 3D Keypoint Descriptors for Non-rigid Shape Matching." In Computer Vision – ECCV 2018, 3–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01237-3_1.

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Golyanik, Vladislav. "Shape Priors in Dense Non-Rigid Structure from Motion." In Robust Methods for Dense Monocular Non-Rigid 3D Reconstruction and Alignment of Point Clouds, 89–133. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-30567-3_5.

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Pérez de la Blanca, Nicolás, and Antonio Garrido. "Recovering Non-rigid 3D Shape Using a Plane+Parallax Approach." In Articulated Motion and Deformable Objects, 251–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36138-3_21.

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Shi, Xiangfu, Jieyu Zhao, Long Zhang, and Xulun Ye. "Non-rigid 3D Object Retrieval with a Learned Shape Descriptor." In Lecture Notes in Computer Science, 24–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71589-6_3.

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Wu, Yujuan, Haisheng Li, Yujia Du, and Qiang Cai. "Non-rigid 3D Shape Classification Based on Low-Level Features." In Proceedings of 2018 Chinese Intelligent Systems Conference, 651–59. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2288-4_62.

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Chiotellis, Ioannis, Rudolph Triebel, Thomas Windheuser, and Daniel Cremers. "Non-rigid 3D Shape Retrieval via Large Margin Nearest Neighbor Embedding." In Computer Vision – ECCV 2016, 327–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46475-6_21.

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Wang, Quan, Fei Wang, Daming Li, and Xuan Wang. "Clustering-Based Latent Variable Models for Monocular Non-rigid 3D Shape Recovery." In Intelligent Computing Methodologies, 162–72. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09339-0_17.

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Yu, Ruixuan, Jian Sun, and Huibin Li. "Learning Spectral Transform Network on 3D Surface for Non-rigid Shape Analysis." In Lecture Notes in Computer Science, 377–94. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11015-4_28.

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Тези доповідей конференцій з теми "3D non-rigid shapes"

Koh, Sung Shik, Thi Thi Zin, and Hiromitsu Hama. "Accurate Reconstruction of Non-rigid 3D Shapes." In 2007 Digest of Technical Papers International Conference on Consumer Electronics. IEEE, 2007. http://dx.doi.org/10.1109/icce.2007.341495.

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Kuang, Zhenzhong, Zongmin Li, Xiaxia Jiang, and Yujie Liu. "Graph Contexts for Retrieving Deformable Non-rigid 3D Shapes." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.486.

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Xiong, Yuehan, and Hongkai Xiong. "Graph-Based Descriptor Learning for Non-Rigid 3D Shapes." In 2019 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2019. http://dx.doi.org/10.1109/iscas.2019.8702733.

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Aouada, Djamila, David W. Dreisigmeyer, and Hamid Krim. "Geometric modeling of rigid and non-rigid 3D shapes using the global geodesic function." In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops). IEEE, 2008. http://dx.doi.org/10.1109/cvprw.2008.4563075.

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Rakprayoon, Panjawee, and Miti Ruchanurucks. "An Adaptive Descriptor for Functional Correspondence Between Non-Rigid 3D Shapes." In 2018 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2018. http://dx.doi.org/10.1109/wiecon-ece.2018.8783183.

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Wu, Huai-Yu, and Hongbin Zha. "Robust consistent correspondence between 3D non-rigid shapes based on “Dual Shape-DNA”." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126292.

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Keshavarzi, Vahid, and Farshid Hajati. "Non-rigid 3D shapes retrieval using a hybrid algorithm based on local descriptors." In 2017 10th Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE, 2017. http://dx.doi.org/10.1109/iranianmvip.2017.8342337.

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Abdelrahman, Mostafa, Aly Farag, David Swanson, and Moumen T. El-Melegy. "Heat diffusion over weighted manifolds: A new descriptor for textured 3D non-rigid shapes." In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015. http://dx.doi.org/10.1109/cvpr.2015.7298614.

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Lindau, Björn, Kristina Wärmefjord, Lars Lindkvist, and Rikard Söderberg. "Virtual Fixturing: Inspection of a Non-Rigid Detail Resting on 3-Points to Estimate Free State and Over-Constrained Shapes." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24515.

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Abstract When the geometry of a non-rigid part or pre-assembly is measured fully clamped (over-constrained) in a measurement fixture, the spring-back information and influence from gravity forces are usually lost in the collected data. From the 3D-measurement data, it is hard to understand built in tensions, and the detail’s tendency to bend, twist and warp after release from the measurement fixture. These effects are however important to consider when analyzing each part’s contribution to geometrical deviations after assembly. In this paper a method is presented, describing how free state shape and over-constrained shape of a measured detail can be virtually estimated starting from acquired data when the part or the preassembly is resting on only 3-points. The objective is to minimize the information loss, to spare measurement resources and to allow for a wider use of the collected data, describing the geometry. Part stiffnesses, part to part contacts and gravity effects are considered in the proposed method. The method is based on 3D-scanning techniques to acquire the shape of the measured object. Necessary compensations for part stiffnesses and gravity effects are based upon Finite Element Analysis (FEA) and the Method of Influence Coefficients (MIC). The presented method is applied to an industrial case to demonstrate its potential. The results show that estimated over-constrained shapes show good resemblance with measurements acquired when part is over-constrained in its measurement fixture.

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Jindal, Rahul, and Nabanita Datta. "Free Dry and Wet Vibration of 2-Way Tapered Hollow Marine Rudder With Non-Classical Pivot: Theoretical Study." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41106.

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A theoretical analysis of free dry and wet vibration of a trapezoidal, 2-way tapered, marine spade rudder, is presented. The rudder is considered as a hollow Kirchhoff’s plate, with the chord section as a NACA profile. The chord length and the thickness taper from the top to the bottom, over the vertical span. The rudder is pivoted at the top, with the pivot behind the leading edge. The pivot is modeled as a combination of a translational and a rotational spring, in order to include the rigid body modes of the rudder vibration. The span-wise and chord-wise non-uniform beam vibration is first analyzed by the Rayleigh-Ritz method, in order to establish the non-uniform beam mode shapes. The span-wise beam is a linearly tapered vertical cantilever, with non-classical edge at the top and free at the bottom. The chord-wise section is a 2-span beam with the ends free, and four continuity conditions at the pivot. The non-uniform mode shapes, in either direction, are a weighted summation of the uniform beam mode shapes, which also satisfy the boundary/continuity conditions. They now act as admissible spatial functions to the plate vibration, which is analyzed by the Galerkin’s method. Eigenvalue analysis generates the plate natural frequencies. A weighted superposition, of the product of the beam mode shapes, in either direction, generates the plate mode shapes. Alternately, uniform beam mode shapes are used as admissible functions into the Galerkin’s method for the plate natural frequencies and mode shapes. The natural frequencies are generated for various positions of the rudder stock along the chord length. The pivot conditions (in both translational and rotational rigid body degree of freedom) influence the prominence of the rigid body mode shapes. The natural frequencies are analyzed for various pivot fixities, taper ratios, and aspect ratios of the plate. This is followed by the wet vibration analysis of the rudder. First, 2D strip theory is used to generate the added mass of each chord section. Constant strength source distribution technique is used to generate the added mass in sway and yaw of a 2D aerofoil. Each flexural and torsional mode is associated with its own added mass. Various empirical corrections are done to account for the 3D flow. Finally, 3D panel method is used to generate the modal added masses, and hence the wet natural frequencies. The added mass coefficient is generated for various aerofoil fineness ratios, pivot fixities, taper ratios, and aspect ratios of the plate.

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