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.
@article{arxiv.2312.08364,
title = {View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data},
author = {Zeyu Ma and Alexander Raistrick and Lahav Lipson and Jia Deng},
journal= {arXiv preprint arXiv:2312.08364},
year = {2023}
}