Procedural Dataset Generation for Zero-Shot Stereo Matching

• URL: http://arxiv.org/abs/2504.16930v1
• Date: Wed, 23 Apr 2025 17:59:33 GMT
• Title: Procedural Dataset Generation for Zero-Shot Stereo Matching
• Authors: David Yan, Alexander Raistrick, Jia Deng,
• Abstract summary: We develop a procedural generator specifically optimized for zero-shot stereo datasets.<n>We report the effects on zero-shot stereo matching performance using standard benchmarks.<n>We open source our system to enable further research on procedural stereo datasets.
• Score: 62.21867807221371
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Synthetic datasets are a crucial ingredient for training stereo matching networks, but the question of what makes a stereo dataset effective remains largely unexplored. We investigate the design space of synthetic datasets by varying the parameters of a procedural dataset generator, and report the effects on zero-shot stereo matching performance using standard benchmarks. We collect the best settings to produce Infinigen-Stereo, a procedural generator specifically optimized for zero-shot stereo datasets. Models trained only on data from our system outperform robust baselines trained on a combination of existing synthetic datasets and have stronger zero-shot stereo matching performance than public checkpoints from prior works. We open source our system at https://github.com/princeton-vl/InfinigenStereo to enable further research on procedural stereo datasets.

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