Academic literature on the topic 'Gaussian splatting'

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Journal articles on the topic "Gaussian splatting"

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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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Dissertations / Theses on the topic "Gaussian splatting"

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Dey, Arnab. "Rendu neuronal pour la représentation humaine en 3D avec des caractéristiques biomécaniques." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4036.

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La représentation numérique des scènes réel en général, et des personnes en particulier, est depuis longtemps un domaine de recherche important en raison de ses applications variées. Les avatars humains réalistes sont essentiels pour les applications en diagnostic médical, de réalité augmentée/virtuelle (AR/VR) et dans l'industrie du divertissement. Ces avatars doivent représenter avec précision la géométrie humaine ainsi que la texture en accord avec les propriétés biomécaniques des humains. Cette thèse présente des techniques innovantes pour générer efficacement et de manière réalistes des a
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Book chapters on the topic "Gaussian splatting"

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Lee, Byeonghyeon, Howoong Lee, Xiangyu Sun, Usman Ali, and Eunbyung Park. "Deblurring 3D Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73636-0_8.

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Saroha, Abhishek, Mariia Gladkova, Cecilia Curreli, Dominik Muhle, Tarun Yenamandra, and Daniel Cremers. "Gaussian Splatting in Style." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85187-2_15.

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Zhao, Lingzhe, Peng Wang, and Peidong Liu. "BAD-Gaussians: Bundle Adjusted Deblur Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72698-9_14.

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Rota Bulò, Samuel, Lorenzo Porzi, and Peter Kontschieder. "Revising Densification in Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73036-8_20.

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Liang, Zhihao, Qi Zhang, Wenbo Hu, Lei Zhu, Ying Feng, and Kui Jia. "Analytic-Splatting: Anti-Aliased 3D Gaussian Splatting via Analytic Integration." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72643-9_17.

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Ververas, Evangelos, Rolandos Alexandros Potamias, Jifei Song, Jiankang Deng, and Stefanos Zafeiriou. "SAGS: Structure-Aware 3D Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-72655-2_13.

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Yan, Yunzhi, Haotong Lin, Chenxu Zhou, et al. "Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-73464-9_10.

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Bae, Jeongmin, Seoha Kim, Youngsik Yun, Hahyun Lee, Gun Bang, and Youngjung Uh. "Per-Gaussian Embedding-Based Deformation for Deformable 3D Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72633-0_18.

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Chang, Jiahao, Yinglin Xu, Yihao Li, Yuantao Chen, Wensen Feng, and Xiaoguang Han. "GaussReg: Fast 3D Registration with Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72633-0_23.

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Wang, Yuxuan, Xuanyu Yi, Zike Wu, Na Zhao, Long Chen, and Hanwang Zhang. "View-Consistent 3D Editing with Gaussian Splatting." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72761-0_23.

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Conference papers on the topic "Gaussian splatting"

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Matsuki, Hidenobu, Riku Murai, Paul H. J. Kelly, and Andrew J. Davison. "Gaussian Splatting SLAM." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01708.

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Zanjani, Farhad G., Hong Cai, Hanno Ackermann, Leila Mirvakhabova, and Fatih Porikli. "Planar Gaussian Splatting." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00863.

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Yu, Zehao, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. "Mip-Splatting: Alias-Free 3D Gaussian Splatting." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01839.

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Yu, Heng, Joel Julin, Zoltán Á. Milacski, Koichiro Niinuma, and László A. Jeni. "CoGS: Controllable Gaussian Splatting." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02043.

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Zhang, Dayou, Zhicheng Liang, Zijian Cao, Dan Wang, and Fangxin Wang. "SRBF-Gaussian: Streaming-Optimized 3D Gaussian Splatting." In 2025 IEEE Conference Virtual Reality and 3D User Interfaces (VR). IEEE, 2025. https://doi.org/10.1109/vr59515.2025.00070.

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Qin, Minghan, Wanhua Li, Jiawei Zhou, Haoqian Wang, and Hanspeter Pfister. "LangSplat: 3D Language Gaussian Splatting." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01895.

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Deguchi, Hiroyuki, Mana Masuda, Takuya Nakabayashi, and Hideo Saito. "E2GS: Event Enhanced Gaussian Splatting." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647607.

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Yu, Wenjie, Yan Ding, Bozhi Zhang, Weidong Liang, and Xiujuan Zhu. "Sparse Gaussian Splatting: Dynamic Spatial Sparsification Gaussian Splatting Based on Hash Multi - Resolution Grid." In 2024 10th International Conference on Systems and Informatics (ICSAI). IEEE, 2024. https://doi.org/10.1109/icsai65059.2024.10893785.

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Chen, Zilong, Feng Wang, Yikai Wang, and Huaping Liu. "Text-to-3D using Gaussian Splatting." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02022.

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Wang, Jiaxu, Qiang Zhang, Jingkai Sun, et al. "Reinforcement Learning with Generalizable Gaussian Splatting." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801348.

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