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：高斯重建核由Westover（1990）提出，并于上世纪90年代被计算机图形学界深入研究，为网格和点云之外的物体三维几何表示提供了替代方案。与此同时，当前最先进的（SoTA）可微分渲染器（如Liu等人2019年的工作）采用光栅化方法在每个图像像素上收集三角形或点，并根据视觉距离进行混合。本文提出VoGE，以体积高斯重建核作为几何基元。其渲染管线通过光线追踪捕获最近基元，并沿光线依据体积密度分布进行混合渲染。针对VoGE的高效渲染，我们提出了体积密度聚合的近似闭式解及由粗到精的渲染策略。我们进一步提供了VoGE的CUDA实现，与PyTorch3D相比，可实现具有竞争性渲染速度的实时级别渲染。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库与演示代码已开源至：https://github.com/Angtian/VoGE。