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:
this https URL

本文提出了基于 VoGE（一种利用体积高斯重建核的几何原语）进行渲染的方法，其采用光线追踪捕获最近的原语，并基于它们沿光线的体密度分布混合它们。通过我们提出的亚像素近似公式和渐进式渲染策略，可以高效地进行 VoGE 渲染。与 PyTorch3D 相比，我们提供了 CUDA 实现，使得可以进行实时级别的渲染，研究结果表明 VoGE 可以更好地完成目标姿态估计，形状 / 纹理拟合和遮挡推理等视觉任务。