View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data

• URL: http://arxiv.org/abs/2312.08364v1
• Date: Wed, 13 Dec 2023 18:56:13 GMT
• Title: View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data
• Authors: Zeyu Ma, Alexander Raistrick, Lahav Lipson, Jia Deng
• Abstract summary: Procedural signed distance functions (SDFs) are a powerful tool for modeling large-scale detailed scenes. We propose OcMesher, a mesh extraction algorithm that efficiently handles high-detail unbounded scenes with perfect view-consistency.
• Score: 71.22495169640239
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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