Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density. However, these grid-based approaches lack an explicit understanding of scale and therefore often introduce aliasing, usually in the form of jaggies or missing scene content. Anti-aliasing has previously been addressed by mip-NeRF 360, which reasons about sub-volumes along a cone rather than points along a ray, but this approach is not natively compatible with current grid-based techniques. We show how ideas from rendering and signal processing can be used to construct a technique that combines mip-NeRF 360 and grid-based models such as Instant NGP to yield error rates that are 8% - 77% lower than either prior technique, and that trains 24x faster than mip-NeRF 360.

翻译：神经辐射场训练可以通过在NeRF从空间坐标到颜色和体积密度的学习映射中采用基于网格的表示来加速。然而，这些基于网格的方法缺乏对尺度的明确理解，因此常常引入锯齿伪影（通常表现为锯齿状边缘或场景内容缺失）。抗锯齿问题此前已通过mip-NeRF 360得到解决，该方法沿锥体子体积而非沿射线点进行推理，但该方案与当前基于网格的技术不兼容。我们展示了如何利用渲染和信号处理领域的理论构建一种技术，该技术将mip-NeRF 360与Instant NGP等基于网格的模型相结合，使误差率比任一种先前技术降低8%至77%，同时训练速度比mip-NeRF 360快24倍。