Mesh Extraction for Unbounded Scenes Using Camera-Aware Octrees
Keywords: Mesh extraction, octree, synthetic data
TL;DR: We propose OcMesher, which extracts a memory-efficient and highly detailed mesh for an unbounded scene represented by occupancy functions given a set of camera views.
Abstract: Mesh extraction from occupancy functions is a useful tool in creating synthetic datasets for computer vision. However, existing mesh extraction methods have artifacts or performance profiles that limit their use. We propose OcMesher, a mesh extractor 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 occupancy function and multiple camera views. We performed extensive experiments, and demonstrate OcMesher's usefulness for synthetic training & benchmark datasets, generating real-time environments for embodied AI and mesh extraction from depthmaps or novel view synthesis methods.
Submission Number: 264
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