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Journal articles on the topic 'Gaussian splatting'

Author: Grafiati

Published: 30 November 2024

Last updated: 25 July 2025

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1

Park, Jichan, Jae-Won Suh, and Yuseok Ban. "Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction." Applied Sciences 15, no. 12 (2025): 6769. https://doi.org/10.3390/app15126769.

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Three-Dimensional Gaussian Splatting (3DGS) has revolutionized novel-view synthesis, enabling real-time rendering of high-quality scenes. Two-Dimensional Gaussian Splatting (2DGS) improves geometric accuracy by replacing 3D Gaussians with flat 2D Gaussians. However, the flat nature of 2D Gaussians reduces mesh quality on volumetric surfaces and results in over-smoothed reconstruction. To address this, we propose Dual-Dimensional Gaussian Splatting (DDGS), which integrates both 2D and 3D Gaussians. First, we generalize the homogeneous transformation matrix based on 2DGS to initialize all Gaussi

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Radl, Lukas, Michael Steiner, Mathias Parger, Alexander Weinrauch, Bernhard Kerbl, and Markus Steinberger. "StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering." ACM Transactions on Graphics 43, no. 4 (2024): 1–17. http://dx.doi.org/10.1145/3658187.

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Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting rendering pipeline relies on several simplifications. Notably, reducing Gaussian to 2D splats with a single viewspace depth introduces popping and blending artifacts during view rotation. Addressing this issue requires accurate per-pixel depth computation, yet a full per-pixel sort proves excessively costly compared to a global sort operation. In this paper, we present a novel hierarchical rasterization approach that sy

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SMIRNOV, A. O. "Camera Pose Estimation Using a 3D Gaussian Splatting Radiance Field." Kibernetika i vyčislitelʹnaâ tehnika 216, no. 2(216) (2024): 15–25. http://dx.doi.org/10.15407/kvt216.02.015.

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Introduction. Accurate camera pose estimation is crucial for many applications ranging from robotics to virtual and augmented reality. The process of determining agents pose from a set of observations is called odometry. This work focuses on visual odometry, which utilizes only images from camera as the input data. The purpose of the paper is to demonstrate an approach for small-scale camera pose estimation using 3D Gaussians as the environment representation. Methods. Given the rise of neural volumetric representations for the environment reconstruction, this work relies on Gaussian Splatting

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Gao, Lin, Jie Yang, Bo-Tao Zhang, et al. "Real-time Large-scale Deformation of Gaussian Splatting." ACM Transactions on Graphics 43, no. 6 (2024): 1–17. http://dx.doi.org/10.1145/3687756.

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Neural implicit representations, including Neural Distance Fields and Neural Radiance Fields, have demonstrated significant capabilities for reconstructing surfaces with complicated geometry and topology, and generating novel views of a scene. Nevertheless, it is challenging for users to directly deform or manipulate these implicit representations with large deformations in a real-time fashion. Gaussian Splatting (GS) has recently become a promising method with explicit geometry for representing static scenes and facilitating high-quality and real-time synthesis of novel views. However, it can

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Xiang, Zhi-Zheng, Chun Xie, and Itaru Kitahara. "BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting." Applied Sciences 15, no. 12 (2025): 6881. https://doi.org/10.3390/app15126881.

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We present a method for enhancing 3D Gaussian Splatting primitives with brushstroke-aware stylization. Previous approaches to 3D style transfer are typically limited to color or texture modifications, lacking an understanding of artistic shape deformation. In contrast, we focus on individual 3D Gaussian primitives, exploring their potential to enable style transfer that incorporates both color- and brushstroke-inspired local geometric stylization. Specifically, we introduce additional texture features for each Gaussian primitive and apply a texture mapping technique to achieve brushstroke-like

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Kasymov, Artur, Bartosz Czekaj, Marcin Mazur, Jacek Tabor, and Przemysław Spurek. "NegGS: Negative Gaussian Splatting." Information Sciences 702 (June 2025): 121912. https://doi.org/10.1016/j.ins.2025.121912.

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Malhotra, Akash, and Nacéra Seghouani. "Gaussian Splatting: An Introduction." Image Processing On Line 15 (June 17, 2025): 45–58. https://doi.org/10.5201/ipol.2025.566.

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Jäger, Miriam, Theodor Kapler, Michael Feßenbecker, Felix Birkelbach, Markus Hillemann, and Boris Jutzi. "HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 159–66. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-159-2024.

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Abstract. In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D Gaussian Splatting stands out for its explicit representation of scenes using 3D Gaussians, making it appealing for tasks like 3D point cloud extraction and surface reconstruction. Motivated by its potential, we address the domain of 3D scene reconstruction, aiming to leverage the capabilities of the Microsoft HoloLens 2 for instant 3D Gaussian Splatting. We present HoloGS, a novel workflow utilizing HoloL

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Chen, Meida, Devashish Lal, Zifan Yu, et al. "Large-Scale 3D Terrain Reconstruction Using 3D Gaussian Splatting for Visualization and Simulation." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 49–54. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-49-2024.

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Abstract. The fusion of low-cost unmanned aerial systems (UAS) with advanced photogrammetric techniques has revolutionized 3D terrain reconstruction, enabling the automated creation of detailed models. Concurrently, the advent of 3D Gaussian Splatting has introduced a paradigm shift in 3D data representation, offering visually realistic renditions distinct from traditional polygon-based models. Our research builds upon this foundation, aiming to integrate Gaussian Splatting into interactive simulations for immersive virtual environments. We address challenges such as collision detection by ado

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Do, Tam Le Phuc, Jinwon Choi, Viet Quoc Le, Philippe Gentet, Leehwan Hwang, and Seunghyun Lee. "HoloGaussian Digital Twin: Reconstructing 3D Scenes with Gaussian Splatting for Tabletop Hologram Visualization of Real Environments." Remote Sensing 16, no. 23 (2024): 4591. https://doi.org/10.3390/rs16234591.

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Several studies have explored the use of hologram technology in architecture and urban design, demonstrating its feasibility. Holograms can represent 3D spatial data and offer an immersive experience, potentially replacing traditional methods such as physical 3D and offering a promising alternative to mixed-reality display technologies. Holograms can visualize realistic scenes such as buildings, cityscapes, and landscapes using the novel view synthesis technique. This study examines the suitability of spatial data collected through the Gaussian splatting method for tabletop hologram visualizat

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Kong, Hanyang, Xingyi Yang, and Xinchao Wang. "Efficient Gaussian Splatting for Monocular Dynamic Scene Rendering via Sparse Time-Variant Attribute Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4374–82. https://doi.org/10.1609/aaai.v39i4.32460.

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Rendering dynamic scenes from monocular videos is a crucial yet challenging task. The recent deformable Gaussian Splatting has emerged as a robust solution to represent real-world dynamic scenes. However, it often leads to heavily redundant Gaussians, attempting to fit every training view at various time steps, leading to slower rendering speeds. Additionally, the attributes of Gaussians in static areas are time-invariant, making it unnecessary to model every Gaussian, which can cause jittering in static regions. In practice, the primary bottleneck in rendering speed for dynamic scenes is the

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Previtali, Mattia, Luigi Barazzetti, and Fabio Roncoroni. "Orthophoto generation with gaussian splatting: mitigating reflective surface artifacts in imagery from low-cost sensors." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W8-2024 (December 14, 2024): 371–78. https://doi.org/10.5194/isprs-archives-xlviii-2-w8-2024-371-2024.

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Abstract. Orthophotos offer a distortion-free and high-resolution depiction of architectural and mechanical elements, serving critical functions in reverse engineering, defect detection, and condition assessment. Recent advancements in low-cost sensors have made them increasingly popular for orthophoto generation due to their affordability and ease of use. However, reflective surfaces pose significant challenges in traditional photogrammetric workflows, leading to inaccuracies in feature matching and 3D reconstruction. This paper investigates the integration of Gaussian splatting into the orth

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Zhou, Jingqiu, Lue Fan, Xuesong Chen, Linjiang Huang, Si Liu, and Hongsheng Li. "GaussianPainter: Painting Point Cloud into 3D Gaussians with Normal Guidance." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10788–96. https://doi.org/10.1609/aaai.v39i10.33172.

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In this paper, we present GaussianPainter, the first method to paint a point cloud into 3D Gaussians given a reference image. GaussianPainter introduces an innovative feed-forward approach to overcome the limitations of time-consuming test-time optimization in 3D Gaussian splatting. Our method addresses a critical challenge in the field: the non-uniqueness problem inherent in the large parameter space of 3D Gaussian splatting. This space, encompassing rotation, anisotropic scales, and spherical harmonic coefficients, introduces the challenge of rendering similar images from substantially diffe

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Franke, Linus, Laura Fink, and Marc Stamminger. "VR-Splatting: Foveated Radiance Field Rendering via 3D Gaussian Splatting and Neural Points." Proceedings of the ACM on Computer Graphics and Interactive Techniques 8, no. 1 (2025): 1–21. https://doi.org/10.1145/3728302.

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Recent advances in novel view synthesis have demonstrated impressive results in fast photorealistic scene rendering through differentiable point rendering, either via Gaussian Splatting (3DGS) [Kerbl et al. 2023 ] or neural point rendering [Aliev et al. 2020 ]. Unfortunately, these directions require either a large number of small Gaussians or expensive per-pixel post-processing for reconstructing fine details, which negatively impacts rendering performance. To meet the high performance demands of virtual reality (VR) systems, primitive or pixel counts therefore must be kept low, affecting vis

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Lyu, Xiaoyang, Yang-Tian Sun, Yi-Hua Huang, et al. "3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting." ACM Transactions on Graphics 43, no. 6 (2024): 1–12. http://dx.doi.org/10.1145/3687952.

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In this paper, we present an implicit surface reconstruction method with 3D Gaussian Splatting (3DGS), namely 3DGSR, that allows for accurate 3D reconstruction with intricate details while inheriting the high efficiency and rendering quality of 3DGS. The key insight is to incorporate an implicit signed distance field (SDF) within 3D Gaussians for surface modeling, and to enable the alignment and joint optimization of both SDF and 3D Gaussians. To achieve this, we design coupling strategies that align and associate the SDF with 3D Gaussians, allowing for unified optimization and enforcing surfa

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Du, Yu, Zhisheng Zhang, Peng Zhang, Fuchun Sun, and Xiao Lv. "UDR-GS: Enhancing Underwater Dynamic Scene Reconstruction with Depth Regularization." Symmetry 16, no. 8 (2024): 1010. http://dx.doi.org/10.3390/sym16081010.

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Representing and rendering dynamic underwater scenes present significant challenges due to the medium’s inherent properties, which result in image blurring and information ambiguity. To overcome these challenges and accomplish real-time rendering of dynamic underwater environments while maintaining efficient training and storage, we propose Underwater Dynamic Scene Reconstruction Gaussian Splatting (UDR-GS), a method based on Gaussian Splatting. By leveraging prior information from a pre-trained depth estimation model and smoothness constraints between adjacent images, our approach uses the es

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Hu, Jintong, Bin Xia, Bin Chen, Wenming Yang, and Lei Zhang. "GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 3554–62. https://doi.org/10.1609/aaai.v39i4.32369.

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Implicit neural representations (INRs) have revolutionized arbitrary-scale super-resolution (ASSR) by modeling images as continuous functions. Most existing INR-based ASSR networks first extract features from the given low-resolution image using an encoder, and then render the super-resolved result via a multi-layer perceptron decoder. Although these approaches have shown promising results, their performance is constrained by the limited representation ability of discrete latent codes in the encoded features. In this paper, we propose a novel ASSR method named GaussianSR that overcomes this li

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Tu, Xuechang, Lukas Radl, Michael Steiner, Markus Steinberger, Bernhard Kerbl, and Fernando de la Torre. "VRSplat: Fast and Robust Gaussian Splatting for Virtual Reality." Proceedings of the ACM on Computer Graphics and Interactive Techniques 8, no. 1 (2025): 1–22. https://doi.org/10.1145/3728311.

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3D Gaussian Splatting (3DGS) has rapidly become a leading technique for novel-view synthesis, providing exceptional performance through efficient software-based GPU rasterization. Its versatility enables real-time applications, including on mobile and lower-powered devices. However, 3DGS faces key challenges in virtual reality (VR): (1) temporal artifacts, such as popping during head movements, (2) projection-based distortions that result in disturbing and view-inconsistent floaters, and (3) reduced framerates when rendering large numbers of Gaussians, falling below the critical threshold for

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Li, Xiaoqiao, Renjie Li, Cheuk-Kit Chung, Ho-Yin Ma, and Chi-Fu Lai. "Reimagining Creativity: Gaussian Splatting as an Organological Extension of Imagination in Artistic Processes." Leonardo 58, no. 3 (2025): 241–49. https://doi.org/10.1162/leon_a_02686.

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Abstract This paper endeavors to investigate how we can understand creativity concerning computation by rearticulating imagination in artistic practices. Drawing on Yuk Hui’s concept of artificial imagination and Bernard Stiegler’s thinking on organology the paper uses the Choi Hung Estate project as a case study demonstrating the potential of Gaussian splatting as a supplement for the imagination in producing dynamic, interactive digital artworks that open the path for conceiving collective memories. By thinking of creativity and beyond computation, the paper unpacks the possibilities for re-

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Henriques, Horácio, Alan de Oliveira, Eder Oliveira, Esteban Clua, and Daniela Trevisan. "Comparing Perceptual Visual Quality of Hybrid Neural and Ray Tracing in Foveated Rendering." Journal on Interactive Systems 16, no. 1 (2025): 328–42. https://doi.org/10.5753/jis.2025.5593.

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Enabling Foveated Rendering for VR devices displays is fundamental when dealing with real-time ray tracing. Combining traditional methods with Neural based strategies, such as NeRFs and 3D Gaussian Splatting, may impact on leveraging performance even more. In this work we enhance and validate how well our traditional Instant-NeRF reconstructs common ray-traced effects through user metrics and quality metrics. We also show that 3D Gaussian Splatting used in the periphery vision area presents better results than the perceptual quality achieved through NeRFs. We present a deep human perception ex

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Smirnov, Anton О. "Dynamic map management for Gaussian Splatting SLAM." Control Systems and Computers, no. 2 (306) (July 2024): 3–9. http://dx.doi.org/10.15407/csc.2024.02.003.

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Map representation and management for Simultaneous Localization and Mapping (SLAM) systems is at the core of such algorithms. Being able to efficiently construct new KeyFrames (KF), remove redundant ones, constructing covisibility graphs has direct impact on the performance and accuracy of SLAM. In this work we outline the algorithm for maintaining dynamic map and its management for SLAM algorithm based on Gaussian Splatting as the environment representation. Gaussian Splatting allows for high-fidelity photorealistic environment reconstruction using differentiable rasterization and is able to

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Lin, Jiaqi, Zhihao Li, Binxiao Huang, et al. "Decoupling Appearance Variations with 3D Consistent Features in Gaussian Splatting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5236–44. https://doi.org/10.1609/aaai.v39i5.32556.

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Gaussian Splatting has emerged as a prominent 3D representation in novel view synthesis, but it still suffers from appearance variations, which are caused by various factors, such as modern camera ISPs, different time of day, weather conditions, and local light changes. These variations can lead to floaters and color distortions in the rendered images/videos. Recent appearance modeling approaches in Gaussian Splatting are either tightly coupled with the rendering process, hindering real-time rendering, or they only account for mild global variations, performing poorly in scenes with local ligh

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Yang, Zhihao, Weilong Peng, and Meie Fang. "Garment-aware gaussian for clothed human modeling from monocular video." Metaverse 6, no. 2 (2025): 3146. https://doi.org/10.54517/m3146.

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<p>Reconstructing the human body from monocular video input presents significant challenges, including a limited field of view and difficulty in capturing non-rigid deformations, such as those associated with clothing and pose variations. These challenges often compromise motion editability and rendering quality. To address these issues, we propose a cloth-aware 3D Gaussian splatting approach that leverages the strengths of 2D convolutional neural networks (CNNs) and 3D Gaussian splatting for high-quality human body reconstruction from monocular video. Our method parameterizes 3D Gaussia

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Zou, Chen, Qingsen Ma, Jia Wang, Rongfeng Lu, Ming Lu, and Zhaowei Qu. "TGA-GS: Thermal Geometrically Accurate Gaussian Splatting." Applied Sciences 15, no. 9 (2025): 4666. https://doi.org/10.3390/app15094666.

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Novel view synthesis and 3D reconstruction have been extensively studied. Three-dimensional Gaussian Splatting (3DGS) has gained popularity due to its rapid training and real-time rendering capabilities. However, RGB imaging is highly dependent on ideal illumination conditions. In low-light situations such as at night or in the presence of occlusions, RGB images often suffer from blurred contours or even complete failure in imaging, which severely restricts the application of 3DGS in such scenarios. Thermal imaging technology, on the other hand, serves as an effective complement. Thermal image

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Sun, Xiaohan, Yinghan Xu, John Dingliana, and Carol O’Sullivan. "CrowdSplat: exploring Gaussian Splatting for crowd rendering." IET Conference Proceedings 2024, no. 10 (2024): 311–14. https://doi.org/10.1049/icp.2024.3321.

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Tan, Zhen, Zhongyan Niu, Jingpu Zhang, Xieyuanli Chen, and Dewen Hu. "New opportunities in SLAM-Gaussian splatting technology." Journal of Image and Graphics 30, no. 6 (2025): 1792–807. https://doi.org/10.11834/jig.240443.

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Kerbl, Bernhard, Andreas Meuleman, Georgios Kopanas, Michael Wimmer, Alexandre Lanvin, and George Drettakis. "A Hierarchical 3D Gaussian Representation for Real-Time Rendering of Very Large Datasets." ACM Transactions on Graphics 43, no. 4 (2024): 1–15. http://dx.doi.org/10.1145/3658160.

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Novel view synthesis has seen major advances in recent years, with 3D Gaussian splatting offering an excellent level of visual quality, fast training and real-time rendering. However, the resources needed for training and rendering inevitably limit the size of the captured scenes that can be represented with good visual quality. We introduce a hierarchy of 3D Gaussians that preserves visual quality for very large scenes, while offering an efficient Level-of-Detail (LOD) solution for efficient rendering of distant content with effective level selection and smooth transitions between levels. We

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Nazarenus, Jakob, Simin Kou, Fang-Lue Zhang, and Reinhard Koch. "Arbitrary Optics for Gaussian Splatting Using Space Warping." Journal of Imaging 10, no. 12 (2024): 330. https://doi.org/10.3390/jimaging10120330.

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Due to recent advances in 3D reconstruction from RGB images, it is now possible to create photorealistic representations of real-world scenes that only require minutes to be reconstructed and can be rendered in real time. In particular, 3D Gaussian splatting shows promising results, outperforming preceding reconstruction methods while simultaneously reducing the overall computational requirements. The main success of 3D Gaussian splatting relies on the efficient use of a differentiable rasterizer to render the Gaussian scene representation. One major drawback of this method is its underlying p

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Zhu, Lingting, Guying Lin, Jinnan Chen, et al. "Large Images Are Gaussians: High-Quality Large Image Representation with Levels of 2D Gaussian Splatting." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 10 (2025): 10977–85. https://doi.org/10.1609/aaai.v39i10.33193.

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While Implicit Neural Representations (INRs) have demonstrated significant success in image representation, they are often hindered by large training memory and slow decoding speed. Recently, Gaussian Splatting (GS) has emerged as a promising solution in 3D reconstruction due to its highquality novel view synthesis and rapid rendering capabilities, positioning it as a valuable tool for a broad spectrum of applications. In particular, a GS-based representation, 2DGS, has shown potential for image fitting. In our work, we present Large Images are Gaussians (LIG), which delves deeper into the app

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Papantonakis, Panagiotis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. "Reducing the Memory Footprint of 3D Gaussian Splatting." Proceedings of the ACM on Computer Graphics and Interactive Techniques 7, no. 1 (2024): 1–17. http://dx.doi.org/10.1145/3651282.

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3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues

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Dong, Zheng, Ke Xu, Yaoan Gao, Hujun Bao, Weiwei Xu, and Rynson W. H. Lau. "Gaussian Surfel Splatting for Live Human Performance Capture." ACM Transactions on Graphics 43, no. 6 (2024): 1–17. http://dx.doi.org/10.1145/3687993.

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High-quality real-time rendering using user-affordable capture rigs is an essential property of human performance capture systems for real-world applications. However, state-of-the-art performance capture methods may not yield satisfactory rendering results under a very sparse (e.g., four) capture setting. Specifically, neural radiance field (NeRF)-based methods and 3D Gaussian Splatting (3DGS)-based methods tend to produce local geometry errors for unseen performers, while occupancy field (PIFu)-based methods often produce unrealistic rendering results. In this paper, we propose a novel gener

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Jiang, Yuheng, Zhehao Shen, Yu Hong, et al. "Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos." ACM Transactions on Graphics 43, no. 6 (2024): 1–15. http://dx.doi.org/10.1145/3687926.

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Volumetric video represents a transformative advancement in visual media, enabling users to freely navigate immersive virtual experiences and narrowing the gap between digital and real worlds. However, the need for extensive manual intervention to stabilize mesh sequences and the generation of excessively large assets in existing workflows impedes broader adoption. In this paper, we present a novel Gaussian-based approach, dubbed DualGS , for real-time and high-fidelity playback of complex human performance with excellent compression ratios. Our key idea in DualGS is to separately represent mo

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Qiu, Shiyu, Chunlei Wu, Zhenghao Wan, and Siyuan Tong. "High-Fold 3D Gaussian Splatting Model Pruning Method Assisted by Opacity." Applied Sciences 15, no. 3 (2025): 1535. https://doi.org/10.3390/app15031535.

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Recent advancements in 3D scene representation have underscored the potential of Neural Radiance Fields (NeRFs) for producing high-fidelity renderings of complex scenes. However, NeRFs are hindered by the significant computational burden of volumetric rendering. To address this, 3D Gaussian Splatting (3DGS) has emerged as an efficient alternative, utilizing Gaussian-based representations and rasterization techniques to achieve faster rendering speeds without sacrificing image quality. Despite these advantages, the large number of Gaussian points and associated internal parameters result in hig

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Cui, Jiadi, Junming Cao, Fuqiang Zhao, et al. "LetsGo: Large-Scale Garage Modeling and Rendering via LiDAR-Assisted Gaussian Primitives." ACM Transactions on Graphics 43, no. 6 (2024): 1–18. http://dx.doi.org/10.1145/3687762.

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Large garages are ubiquitous yet intricate scenes that present unique challenges due to their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle glass. Conventional Structure from Motion (SfM) methods for camera pose estimation and 3D reconstruction often fail in these environments due to poor correspondence construction. To address these challenges, we introduce LetsGo, a LiDAR-assisted Gaussian splatting framework for large-scale garage modeling and rendering. We develop a handheld scanner, Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate

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Guo, Chaoyang, Chunyan Gao, Yiyang Bai, and Xiaoling Lv. "RD-SLAM: Real-Time Dense SLAM Using Gaussian Splatting." Applied Sciences 14, no. 17 (2024): 7767. http://dx.doi.org/10.3390/app14177767.

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Simultaneous localization and mapping (SLAM) is fundamental for intelligent mobile units to perform diverse tasks. Recent work has shown that integrating neural rendering and SLAM showed promising results in photorealistic environment reconstruction. However, existing methods estimate pose by minimizing the error between rendered and input images, which is time-consuming and cannot be run in real-time, deviating from the original intention of SLAM. In this paper, we propose a dense RGB-D SLAM based on 3D Gaussian splatting (3DGS) while employing generalized iterative closest point (G-ICP) for

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Lin, Haian, and Yinwei Zhan. "GaussianAvatar: Human avatar Gaussian splatting from monocular videos." Computers & Graphics 126 (February 2025): 104155. https://doi.org/10.1016/j.cag.2024.104155.

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Nguyen, Van Minh, Emma Sandidge, Trupti Mahendrakar, and Ryan T. White. "Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting." Aerospace 11, no. 3 (2024): 183. http://dx.doi.org/10.3390/aerospace11030183.

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The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that

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Lin, Yangkai, Jiabao Lei, and Kui Jia. "Multi-StyleGS: Stylized Gaussian Splatting with Multiple Styles." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5289–97. https://doi.org/10.1609/aaai.v39i5.32562.

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In recent years, there has been a growing demand to stylize a given 3D scene to align with the artistic style of reference images for creative purposes. While 3D Gaussian Splatting (GS) has emerged as a promising and efficient method for realistic 3D scene modeling, there remains a challenge in adapting it to stylize 3D GS to match with multiple styles through automatic local style transfer or manual designation, while maintaining memory efficiency for stylization training. In this paper, we introduce a novel 3D GS stylization solution termed Multi-StyleGS to tackle these challenges. In partic

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Qin, Xiaofei, Haoying Ye, Changxiang He, and Xuedian Zhang. "Deblur-aware Gaussian splatting simultaneous localization and mapping." Knowledge-Based Systems 316 (May 2025): 113366. https://doi.org/10.1016/j.knosys.2025.113366.

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Liu, Yuxiang, Xi Chen, Shen Yan, et al. "ThermalGS: Dynamic 3D Thermal Reconstruction with Gaussian Splatting." Remote Sensing 17, no. 2 (2025): 335. https://doi.org/10.3390/rs17020335.

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Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. Current TIR image-based 3D reconstruction methods primarily focus on static conditions, which only capture the spatial distribution of thermal radiation but lack the ability to represent its temporal dynamics. The absence of dedicated datasets and effective methods for dynamic 3D representation are two key challenges that hinder progress in this field. To address these challenges, we propose a novel

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Guo, Haipeng, Huanyu Liu, Jiazheng Wen, and Junbao Li. "Cross-Spectral Gaussian Splatting with Spatial Occupancy Consistency." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3229–37. https://doi.org/10.1609/aaai.v39i3.32333.

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Using images captured by cameras with different light spectrum sensitivities, training a unified model for cross-spectral scene representation is challenging. Recent advances have shown the possibility of jointly optimizing cross-spectral relative poses and neural radiance fields using normalized cross-device coordinates. However, such method suffers from cross-spectral misalignment when collecting data asynchronously from devices and lacks the capability to render in real-time or handle large scenes. We address these issues by proposing cross-spectral Gaussian Splatting with spatial occupancy

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Petrovska, Ivana, and Boris Jutzi. "3D Gaussian Splatting Methods for Real-World Scenarios." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 11, 2025): 641–48. https://doi.org/10.5194/isprs-annals-x-g-2025-641-2025.

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Abstract. 3D Gaussian Splatting (3DGS) is an innovative solution for explicit point-based 3D representation where each point is represented as a Gaussian distribution. Using calibrated images and sparse point cloud for initialization, the scene is reconstructed by optimizing the Gaussian position, orientation, shape and appearance. In this contribution, we present a comprehensive overview of 3DGS methods available on complimentary radiance field reconstruction platforms namely, the original 3DGS implementation as reference, Splatfacto, 3DGS-MCMC and 3DGS-LumaAI to address a broader audience in

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Yu, Zehao, Torsten Sattler, and Andreas Geiger. "Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes." ACM Transactions on Graphics 43, no. 6 (2024): 1–13. http://dx.doi.org/10.1145/3687937.

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Recently, 3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results, while allowing the rendering of high-resolution images in real-time. However, leveraging 3D Gaussians for surface reconstruction poses significant challenges due to the explicit and disconnected nature of 3D Gaussians. In this work, we present Gaussian Opacity Fields (GOF), a novel approach for efficient, high-quality, and adaptive surface reconstruction in unbounded scenes. Our GOF is derived from ray-tracing-based volume rendering of 3D Gaussians, enabling direct geometry extraction from 3D Gauss

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Yang, Chen, Sikuang Li, Jiemin Fang, et al. "GaussianObject: High-Quality 3D Object Reconstruction from Four Views with Gaussian Splatting." ACM Transactions on Graphics 43, no. 6 (2024): 1–13. http://dx.doi.org/10.1145/3687759.

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Reconstructing and rendering 3D objects from highly sparse views is of critical importance for promoting applications of 3D vision techniques and improving user experience. However, images from sparse views only contain very limited 3D information, leading to two significant challenges: 1) Difficulty in building multi-view consistency as images for matching are too few; 2) Partially omitted or highly compressed object information as view coverage is insufficient. To tackle these challenges, we propose GaussianObject, a framework to represent and render the 3D object with Gaussian splatting tha

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Yu, Yingwen, Edward Verbree, Peter van Oosterom, and Uta Pottgiesser. "3D Gaussian Splatting for Modern Architectural Heritage: Integrating UAV-Based Data Acquisition and Advanced Photorealistic 3D Techniques." AGILE: GIScience Series 6 (June 9, 2025): 1–9. https://doi.org/10.5194/agile-giss-6-51-2025.

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Abstract. 3D Gaussian Splatting (3DGS) is an advanced 3D representation method that enhances point clouds by incorporating spectral image content, enabling high-fidelity heritage documentation. A 3DGS visualization presents these enriched Gaussians, ensuring high geometric accuracy, detailed texture representation, and efficient spatial reconstruction, thereby enhancing both precision and efficiency in digital heritage preservation. This paper applies 3DGS to modern architectural heritage, combining UAV-based data acquisition with advanced rendering techniques. Using the Delft University of Te

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Sun, Yang, Yue Zhou, Bin Tian, Haiyang Wang, Yongchao Zhao, and Songdi Wu. "Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splatting." Applied Sciences 15, no. 11 (2025): 6262. https://doi.org/10.3390/app15116262.

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Unbounded dynamic scene reconstruction is crucial for applications such as autonomous driving, robotics, and virtual reality. However, existing methods struggle to reconstruct dynamic scenes in unbounded outdoor environments due to challenges such as lighting variation, object motion, and sensor limitations, leading to inaccurate geometry and low rendering fidelity. In this paper, we proposed Gaussian-UDSR, a novel 3D Gaussian-based representation that efficiently reconstructs and renders high-quality, unbounded dynamic scenes in real time. Our approach fused LiDAR point clouds and Structure-f

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Han, Huasong, Kaixuan Zhou, Xiaoxiao Long, Yusen Wang, and Chunxia Xiao. "GGS: Generalizable Gaussian Splatting for Lane Switching in Autonomous Driving." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 3329–37. https://doi.org/10.1609/aaai.v39i3.32344.

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We propose GGS, a Generalizable Gaussian Splatting method for Autonomous Driving that can achieve realistic rendering under large viewpoint changes. Previous generalizable 3D gaussian splatting methods are limited to rendering novel views that are very close to the original pair of images, which cannot handle large difference in viewpoint. Especially in autonomous driving scenarios, images are typically collected from a single lane. The limited training perspective makes rendering images of a different lane very challenging. To further improve the rendering capability of GGS under large viewpo

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Ma, Xiasheng, Ci Song, Yimin Ji, and Shanlin Zhong. "Related Keyframe Optimization Gaussian–Simultaneous Localization and Mapping: A 3D Gaussian Splatting-Based Simultaneous Localization and Mapping with Related Keyframe Optimization." Applied Sciences 15, no. 3 (2025): 1320. https://doi.org/10.3390/app15031320.

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Simultaneous localization and mapping (SLAM) is the basis for intelligent robots to explore the world. As a promising method for 3D reconstruction, 3D Gaussian splatting (3DGS) integrated with SLAM systems has shown significant potential. However, due to environmental uncertainties, errors in the tracking process with 3D Gaussians can negatively impact SLAM systems. This paper introduces a novel dense RGB-D SLAM system based on 3DGS that refines Gaussians through sub-Gaussians in the camera coordinate system. Additionally, we propose an algorithm to select keyframes closely related to the curr

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Lian, Haojie, Kangle Liu, Ruochen Cao, Ziheng Fei, Xin Wen, and Leilei Chen. "Integration of 3D Gaussian Splatting and Neural Radiance Fields in Virtual Reality Fire Fighting." Remote Sensing 16, no. 13 (2024): 2448. http://dx.doi.org/10.3390/rs16132448.

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Neural radiance fields (NeRFs) and 3D Gaussian splatting have emerged as promising 3D reconstruction techniques recently. However, their application in virtual reality (VR), particularly in firefighting training, remains underexplored. We present an innovative VR firefighting simulation system based on 3D Gaussian Splatting technology. Leveraging these techniques, we successfully reconstruct realistic physical environments. By integrating the Unity3D game engine with head-mounted displays (HMDs), we created and presented immersive virtual fire scenes. Our system incorporates NeRF technology to

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Abramov, Nikolai, Havana Lankegowda, Shunwei Liu, Luigi Barazzetti, Carlo Beltracchi, and Pierpaolo Ruttico. "Implementing Immersive Worlds for Metaverse-Based Participatory Design through Photogrammetry and Blockchain." ISPRS International Journal of Geo-Information 13, no. 6 (2024): 211. http://dx.doi.org/10.3390/ijgi13060211.

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This paper explores participatory design methods for the interconnection of digital recording techniques, like digital photogrammetry and Gaussian splatting, with emerging domains such as the metaverse and blockchain technology. The focus lies in community engagement and the economic growth of urban and rural areas through blockchain integration, utilizing low-cost digitalization methods to create Web3 environments mirroring real settlements. Through a case study of an Italian village, the potential of participatory design and community-led development strategies in revitalizing neglected area

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