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.

翻译：过程化合成数据生成在计算机视觉领域受到越来越多的关注。过程化符号距离函数(SDFs)是大规模精细场景建模的有力工具，但现有的网格提取方法存在伪影或性能问题，限制了其在合成数据中的应用。我们提出OcMesher网格提取算法，该算法能够高效处理高细节无界场景，并具有完美的视角一致性，且易于导出至下游实时引擎。我们方法的主要创新在于提出了一种基于给定SDF和多视角的八叉树构建算法。通过大量实验表明，我们的方法能生成更优质的合成数据，用于计算机视觉模型的训练与评估。