  Neural rendering with implicit neural networks has recently emerged as an
attractive proposition for scene reconstruction, achieving excellent quality
albeit at high computational cost. While the most recent generation of such
methods has made progress on the rendering (inference) times, very little
progress has been made on improving the reconstruction (training) times. In
this work, we present Neural Adaptive Scene Tracing (NAScenT), the first neural
rendering method based on directly training a hybrid explicit-implicit neural
representation. NAScenT uses a hierarchical octree representation with one
neural network per leaf node and combines this representation with a two-stage
sampling process that concentrates ray samples where they matter most near
object surfaces. As a result, NAScenT is capable of reconstructing challenging
scenes including both large, sparsely populated volumes like UAV captured
outdoor environments, as well as small scenes with high geometric complexity.
NAScenT outperforms existing neural rendering approaches in terms of both
quality and training time.
