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2D Gaussian Splatting for Geometrically Accurate Radiance Fields

Authors:

Andreas Geiger,

Shenghua GaoAuthors Info & Claims

SIGGRAPH '24: ACM SIGGRAPH 2024 Conference Papers

Article No.: 32, Pages 1 - 11

https://doi.org/10.1145/3641519.3657428

Published: 13 July 2024 Publication History

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Abstract

3D Gaussian Splatting (3DGS) has recently revolutionized radiance field reconstruction, achieving high quality novel view synthesis and fast rendering speed. However, 3DGS fails to accurately represent surfaces due to the multi-view inconsistent nature of 3D Gaussians. We present 2D Gaussian Splatting (2DGS), a novel approach to model and reconstruct geometrically accurate radiance fields from multi-view images. Our key idea is to collapse the 3D volume into a set of 2D oriented planar Gaussian disks. Unlike 3D Gaussians, 2D Gaussians provide view-consistent geometry while modeling surfaces intrinsically. To accurately recover thin surfaces and achieve stable optimization, we introduce a perspective-accurate 2D splatting process utilizing ray-splat intersection and rasterization. Additionally, we incorporate depth distortion and normal consistency terms to further enhance the quality of the reconstructions. We demonstrate that our differentiable renderer allows for noise-free and detailed geometry reconstruction while maintaining competitive appearance quality, fast training speed, and real-time rendering. Project page at https://surfsplatting.github.io.

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References

[1]

Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. 2020. Neural point-based graphics. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII 16. Springer, 696–712.

[2]

Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. 2021. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. ICCV (2021).

[3]

Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. 2022a. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5470–5479.

[4]

Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. 2022b. Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields. CVPR (2022).

[5]

Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, and Peter Hedman. 2023. Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields. ICCV (2023).

[6]

Mario Botsch, Alexander Hornung, Matthias Zwicker, and Leif Kobbelt. 2005. High-quality surface splatting on today’s GPUs. In Proceedings Eurographics/IEEE VGTC Symposium Point-Based Graphics, 2005. IEEE, 17–141.

[7]

Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. 2022. TensoRF: Tensorial Radiance Fields. In European Conference on Computer Vision (ECCV).

[8]

Hanlin Chen, Chen Li, and Gim Hee Lee. 2023b. NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance. arXiv preprint arXiv:2312.00846 (2023).

[9]

Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. 2023a. Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16569–16578.

[10]

Zhang Chen, Zhong Li, Liangchen Song, Lele Chen, Jingyi Yu, Junsong Yuan, and Yi Xu. 2023c. NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4182–4194.

[11]

Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. 2022. Plenoxels: Radiance Fields without Neural Networks. In CVPR.

[12]

Qiancheng Fu, Qingshan Xu, Yew-Soon Ong, and Wenbing Tao. 2022. Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction. Advances in Neural Information Processing Systems (NeurIPS) (2022).

[13]

Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao. 2023. Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition and Ray Tracing. arXiv:2311.16043 (2023).

[14]

Antoine Guédon and Vincent Lepetit. 2023. SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering. arXiv preprint arXiv:2311.12775 (2023).

[15]

Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. 2021. Baking neural radiance fields for real-time view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5875–5884.

[16]

Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma. 2023. Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields. In ICCV.

[17]

Eldar Insafutdinov and Alexey Dosovitskiy. 2018. Unsupervised learning of shape and pose with differentiable point clouds. Advances in neural information processing systems 31 (2018).

[18]

Rasmus Jensen, Anders Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. 2014. Large scale multi-view stereopsis evaluation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 406–413.

Digital Library

[19]

Yingwenqi Jiang, Jiadong Tu, Yuan Liu, Xifeng Gao, Xiaoxiao Long, Wenping Wang, and Yuexin Ma. 2023. GaussianShader: 3D Gaussian Splatting with Shading Functions for Reflective Surfaces. arXiv preprint arXiv:2311.17977 (2023).

[20]

Michael Kazhdan and Hugues Hoppe. 2013. Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG) 32, 3 (2013), 1–13.

Digital Library

[21]

Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 2023. 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Transactions on Graphics 42, 4 (July 2023). https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

Digital Library

[22]

Leonid Keselman and Martial Hebert. 2022. Approximate differentiable rendering with algebraic surfaces. In European Conference on Computer Vision. Springer, 596–614.

Digital Library

[23]

Leonid Keselman and Martial Hebert. 2023. Flexible techniques for differentiable rendering with 3d gaussians. arXiv preprint arXiv:2308.14737 (2023).

[24]

Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. 2017. Tanks and Temples: Benchmarking Large-Scale Scene Reconstruction. ACM Transactions on Graphics 36, 4 (2017).

Digital Library

[25]

Georgios Kopanas, Julien Philip, Thomas Leimkühler, and George Drettakis. 2021. Point-Based Neural Rendering with Per-View Optimization. In Computer Graphics Forum, Vol. 40. Wiley Online Library, 29–43.

[26]

Christoph Lassner and Michael Zollhofer. 2021. Pulsar: Efficient sphere-based neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1440–1449.

[27]

Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. 2023. Neuralangelo: High-Fidelity Neural Surface Reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]

Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, and Kui Jia. 2023. GS-IR: 3D Gaussian Splatting for Inverse Rendering. arXiv preprint arXiv:2311.16473 (2023).

[29]

Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. 2020. Neural Sparse Voxel Fields. NeurIPS (2020).

[30]

Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy Networks: Learning 3D Reconstruction in Function Space. In Conference on Computer Vision and Pattern Recognition (CVPR).

[31]

Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2020. NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis. In ECCV.

[32]

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2021. Nerf: Representing scenes as neural radiance fields for view synthesis. Commun. ACM 65, 1 (2021), 99–106.

Digital Library

[33]

Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. ACM Trans. Graph. 41, 4, Article 102 (July 2022), 15 pages.

Digital Library

[34]

Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. 2020. Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision. In Conference on Computer Vision and Pattern Recognition (CVPR).

[35]

Michael Oechsle, Songyou Peng, and Andreas Geiger. 2021. UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction. In International Conference on Computer Vision (ICCV).

[36]

Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]

Hanspeter Pfister, Matthias Zwicker, Jeroen Van Baar, and Markus Gross. 2000. Surfels: Surface elements as rendering primitives. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques. 335–342.

Digital Library

[38]

Shenhan Qian, Tobias Kirschstein, Liam Schoneveld, Davide Davoli, Simon Giebenhain, and Matthias Nießner. 2023. GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians. arXiv preprint arXiv:2312.02069 (2023).

[39]

Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. 2021. KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs. In International Conference on Computer Vision (ICCV).

[40]

Christian Reiser, Rick Szeliski, Dor Verbin, Pratul Srinivasan, Ben Mildenhall, Andreas Geiger, Jon Barron, and Peter Hedman. 2023. Merf: Memory-efficient radiance fields for real-time view synthesis in unbounded scenes. ACM Transactions on Graphics (TOG) 42, 4 (2023), 1–12.

Digital Library

[41]

Darius Rückert, Linus Franke, and Marc Stamminger. 2022. Adop: Approximate differentiable one-pixel point rendering. ACM Transactions on Graphics (ToG) 41, 4 (2022), 1–14.

Digital Library

[42]

Johannes Lutz Schönberger and Jan-Michael Frahm. 2016. Structure-from-Motion Revisited. In Conference on Computer Vision and Pattern Recognition (CVPR).

[43]

Johannes Lutz Schönberger, Enliang Zheng, Marc Pollefeys, and Jan-Michael Frahm. 2016. Pixelwise View Selection for Unstructured Multi-View Stereo. In European Conference on Computer Vision (ECCV).

[44]

Thomas Schöps, Torsten Sattler, and Marc Pollefeys. 2019. Surfelmeshing: Online surfel-based mesh reconstruction. IEEE transactions on pattern analysis and machine intelligence 42, 10 (2019), 2494–2507.

[45]

Yahao Shi, Yanmin Wu, Chenming Wu, Xing Liu, Chen Zhao, Haocheng Feng, Jingtuo Liu, Liangjun Zhang, Jian Zhang, Bin Zhou, Errui Ding, and Jingdong Wang. 2023. GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization. Arxiv (2023). arXiv:2312.05133

[46]

Christian Sigg, Tim Weyrich, Mario Botsch, and Markus H Gross. 2006. GPU-based ray-casting of quadratic surfaces. In PBG@ SIGGRAPH. 59–65.

[47]

Cheng Sun, Min Sun, and Hwann-Tzong Chen. 2022a. Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction. In CVPR.

[48]

Cheng Sun, Min Sun, and Hwann-Tzong Chen. 2022b. Improved Direct Voxel Grid Optimization for Radiance Fields Reconstruction. arxiv cs.GR 2206.05085 (2022).

[49]

John Vince. 2008. Geometric algebra for computer graphics. Springer Science & Business Media.

[50]

Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. 2021. NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction. Advances in Neural Information Processing Systems 34 (2021), 27171–27183.

[51]

Yiming Wang, Qin Han, Marc Habermann, Kostas Daniilidis, Christian Theobalt, and Lingjie Liu. 2023. NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

[52]

Tim Weyrich, Simon Heinzle, Timo Aila, Daniel B Fasnacht, Stephan Oetiker, Mario Botsch, Cyril Flaig, Simon Mall, Kaspar Rohrer, Norbert Felber, 2007. A hardware architecture for surface splatting. ACM Transactions on Graphics (TOG) 26, 3 (2007), 90–es.

Digital Library

[53]

Thomas Whelan, Renato F Salas-Moreno, Ben Glocker, Andrew J Davison, and Stefan Leutenegger. 2016. ElasticFusion: Real-time dense SLAM and light source estimation. The International Journal of Robotics Research 35, 14 (2016), 1697–1716.

Digital Library

[54]

Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. 2020. SynSin: End-to-end View Synthesis from a Single Image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[55]

Tianyi Xie, Zeshun Zong, Yuxing Qiu, Xuan Li, Yutao Feng, Yin Yang, and Chenfanfu Jiang. 2023. PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics. arXiv preprint arXiv:2311.12198 (2023).

[56]

Yunzhi Yan, Haotong Lin, Chenxu Zhou, Weijie Wang, Haiyang Sun, Kun Zhan, Xianpeng Lang, Xiaowei Zhou, and Sida Peng. 2023. Street Gaussians for Modeling Dynamic Urban Scenes. (2023).

[57]

Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. 2018. MVSNet: Depth Inference for Unstructured Multi-view Stereo. European Conference on Computer Vision (ECCV) (2018).

[58]

Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. 2021. Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems 34 (2021), 4805–4815.

[59]

Lior Yariv, Peter Hedman, Christian Reiser, Dor Verbin, Pratul P. Srinivasan, Richard Szeliski, Jonathan T. Barron, and Ben Mildenhall. 2023. BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis. arXiv (2023).

[60]

Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman. 2020. Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance. Advances in Neural Information Processing Systems 33 (2020).

[61]

Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine-Hornung. 2019. Differentiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1–14.

Digital Library

[62]

Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. 2021. PlenOctrees for Real-time Rendering of Neural Radiance Fields. In ICCV.

[63]

Zehao Yu, Anpei Chen, Bozidar Antic, Songyou Peng, Apratim Bhattacharyya, Michael Niemeyer, Siyu Tang, Torsten Sattler, and Andreas Geiger. 2022a. SDFStudio: A Unified Framework for Surface Reconstruction. https://github.com/autonomousvision/sdfstudio

[64]

Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. 2024. Mip-Splatting: Alias-free 3D Gaussian Splatting. Conference on Computer Vision and Pattern Recognition (CVPR) (2024).

[65]

Zehao Yu and Shenghua Gao. 2020. Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement. In Conference on Computer Vision and Pattern Recognition (CVPR).

[66]

Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger. 2022b. MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction. Advances in Neural Information Processing Systems (NeurIPS) (2022).

[67]

Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. 2020. NeRF++: Analyzing and Improving Neural Radiance Fields. arXiv:2010.07492 (2020).

[68]

Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. 2018. Open3D: A Modern Library for 3D Data Processing. arXiv:1801.09847 (2018).

[69]

Wojciech Zielonka, Timur Bagautdinov, Shunsuke Saito, Michael Zollhöfer, Justus Thies, and Javier Romero. 2023. Drivable 3D Gaussian Avatars. (2023). arxiv:2311.08581 [cs.CV]

[70]

Matthias Zwicker, Hanspeter Pfister, Jeroen Van Baar, and Markus Gross. 2001a. EWA volume splatting. In Proceedings Visualization, 2001. VIS’01. IEEE, 29–538.

[71]

Matthias Zwicker, Hanspeter Pfister, Jeroen Van Baar, and Markus Gross. 2001b. Surface splatting. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques. 371–378.

Digital Library

[72]

Matthias Zwicker, Jussi Rasanen, Mario Botsch, Carsten Dachsbacher, and Mark Pauly. 2004. Perspective accurate splatting. In Proceedings-Graphics Interface. 247–254.

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Index Terms

2D Gaussian Splatting for Geometrically Accurate Radiance Fields
1. Computing methodologies

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cover image ACM Conferences

SIGGRAPH '24: ACM SIGGRAPH 2024 Conference Papers

July 2024

1106 pages

ISBN:9798400705250

DOI:10.1145/3641519

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Published: 13 July 2024

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Jiang CGao RShao KWang YXiong RZhang Y(2025)LI-GS: Gaussian Splatting With LiDAR Incorporated for Accurate Large-Scale ReconstructionIEEE Robotics and Automation Letters10.1109/LRA.2024.352284610:2(1864-1871)Online publication date: Feb-2025
https://doi.org/10.1109/LRA.2024.3522846
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