View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data
CoRR(2023)
摘要
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.
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