We present a super-fast convergence approach to reconstructing the per-scene
radiance field from a set of images that capture the scene with known poses.
This task, which is often applied to novel view synthesis, is recently
revolutionized by Neural Radiance Field (NeRF) for its state-of-the-art quality
and flexibility. However, NeRF and its variants require a lengthy training time
ranging from hours to days for a single scene. In contrast, our approach
achieves NeRF-comparable quality and converges rapidly from scratch in less
than 15 minutes with a single GPU. We adopt a representation consisting of a
density voxel grid for scene geometry and a feature voxel grid with a shallow
network for complex view-dependent appearance. Modeling with explicit and
discretized volume representations is not new, but we propose two simple yet
non-trivial techniques that contribute to fast convergence speed and
high-quality output. First, we introduce the post-activation interpolation on
voxel density, which is capable of producing sharp surfaces in lower grid
resolution. Second, direct voxel density optimization is prone to suboptimal
geometry solutions, so we robustify the optimization process by imposing
several priors. Finally, evaluation on five inward-facing benchmarks shows that
our method matches, if not surpasses, NeRF's quality, yet it only takes about
15 minutes to train from scratch for a new scene.

我们提出了一种超快速收敛的方法，通过从拍摄场景的一组图像中重建场景辐射场来最新的视点合成，该方法采用密度体素网格表示场景几何和 shallow network 表示复杂的视角相关的外观，同时采用了后激活插值和一些先验约束技术，从而使训练时间缩短到 15 分钟以内，并达到了前沿的质量水平。