The Gaussian reconstruction kernels have been proposed by Westover (1990) and studied by the computer graphics community back in the 90s, which gives an alternative representation of object 3D geometry from meshes and point clouds. On the other hand, current state-of-the-art (SoTA) differentiable renderers, Liu et al. (2019), use rasterization to collect triangles or points on each image pixel and blend them based on the viewing distance. In this paper, we propose VoGE, which utilizes the volumetric Gaussian reconstruction kernels as geometric primitives. The VoGE rendering pipeline uses ray tracing to capture the nearest primitives and blends them as mixtures based on their volume density distributions along the rays. To efficiently render via VoGE, we propose an approximate closeform solution for the volume density aggregation and a coarse-to-fine rendering strategy. Finally, we provide a CUDA implementation of VoGE, which enables real-time level rendering with a competitive rendering speed in comparison to PyTorch3D. Quantitative and qualitative experiment results show VoGE outperforms SoTA counterparts when applied to various vision tasks, e.g., object pose estimation, shape/texture fitting, and occlusion reasoning. The VoGE library and demos are available at: https://github.com/Angtian/VoGE.

翻译：Westover（1990）提出的高斯重建内核在90年代已被计算机图形学界广泛研究，提供了网格与点云之外的物体三维几何替代表示方法。另一方面，当前最先进的可微分渲染器（Liu et al., 2019）采用光栅化技术，在图像每个像素上收集三角形或点，并基于观察距离进行混合。本文提出VoGE，该模型以体积高斯重建内核作为几何基元。VoGE渲染管线通过光线追踪捕获最近邻基元，并沿光线方向根据其体积密度分布进行混合。为高效实现VoGE渲染，我们提出了体积密度聚合的近似闭式解与从粗到细的渲染策略。最终，我们提供了VoGE的CUDA实现，可实现实时级渲染，其渲染速度与PyTorch3D具有竞争力。定量与定性实验结果表明，VoGE在物体姿态估计、形状/纹理拟合及遮挡推理等多种视觉任务中均优于当前最先进方法。VoGE库与演示程序见：https://github.com/Angtian/VoGE。