View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for
  Procedural Synthetic Data
Zeyu Ma Alexander Raistrick Lahav Lipson Jia Deng
Procedural synthetic data generation has received increasing attention in
computer vision. Procedural signed distance functions (SDFs) are a powerful
tool for modeling large-scale detailed scenes, but existing mesh extraction
methods have artifacts or performance profiles that limit their use for
synthetic data. We propose OcMesher, a mesh extraction algorithm that
efficiently handles high-detail unbounded scenes with perfect view-consistency,
with easy export to downstream real-time engines. The main novelty of our
solution is an algorithm to construct an octree based on a given SDF and
multiple camera views. We performed extensive experiments, and show our
solution produces better synthetic data for training and evaluation of computer
vision models.