3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians

• URL: http://arxiv.org/abs/2505.24053v1
• Date: Thu, 29 May 2025 22:52:51 GMT
• Title: 3DGEER: Exact and Efficient Volumetric Rendering with 3D Gaussians
• Authors: Zixun Huang, Cho-Ying Wu, Yuliang Guo, Xinyu Huang, Liu Ren,
• Abstract summary: We introduce 3DGEER, an Exact and Efficient Volumetric Gaussian Rendering method.<n>Our method consistently outperforms prior methods, establishing a new state-of-the-art in real-time neural rendering.
• Score: 15.776720879897345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) marks a significant milestone in balancing the quality and efficiency of differentiable rendering. However, its high efficiency stems from an approximation of projecting 3D Gaussians onto the image plane as 2D Gaussians, which inherently limits rendering quality--particularly under large Field-of-View (FoV) camera inputs. While several recent works have extended 3DGS to mitigate these approximation errors, none have successfully achieved both exactness and high efficiency simultaneously. In this work, we introduce 3DGEER, an Exact and Efficient Volumetric Gaussian Rendering method. Starting from first principles, we derive a closed-form expression for the density integral along a ray traversing a 3D Gaussian distribution. This formulation enables precise forward rendering with arbitrary camera models and supports gradient-based optimization of 3D Gaussian parameters. To ensure both exactness and real-time performance, we propose an efficient method for computing a tight Particle Bounding Frustum (PBF) for each 3D Gaussian, enabling accurate and efficient ray-Gaussian association. We also introduce a novel Bipolar Equiangular Projection (BEAP) representation to accelerate ray association under generic camera models. BEAP further provides a more uniform ray sampling strategy to apply supervision, which empirically improves reconstruction quality. Experiments on multiple pinhole and fisheye datasets show that our method consistently outperforms prior methods, establishing a new state-of-the-art in real-time neural rendering.

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