探究合成数据如何提升零样本立体匹配性能，提出高效生成方案。
What Makes Good Synthetic Training Data for Zero-Shot Stereo Matching?
- 通过调整程序化生成器参数，系统研究合成数据设计对性能的影响。
- 仅用新生成数据训练，零样本匹配精度超过主流混合数据集。
- 开源生成代码与参数分析，助力后续立体匹配数据研究。
合成数据是训练立体匹配网络的关键，但什么样的合成数据有效仍缺乏深入研究。本文通过改变程序化数据生成器的参数，系统考察其对零样本立体匹配性能的影响，并在标准基准上验证结果。基于最佳参数组合，我们构建了一个大规模数据集：仅用该数据训练，性能优于多个广泛使用的数据集混合训练，且接近FoundationStereo表现。此外，该方法提供开源生成代码与参数分析，支持后续研究。相关系统已开源至https://github.com/princeton-vl/InfinigenStereo。
原文摘要 · Abstract (English)
Synthetic datasets are a crucial ingredient for training stereo matching networks, but the question of what makes a stereo dataset effective remains underexplored. 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 validate our findings by collecting the best settings and creating a large-scale dataset. Training only on this dataset achieves better performance than training on a mixture of widely used datasets, and is competitive with training on the FoundationStereo dataset, with the additional benefit of open-source generation code and an accompanying parameter analysis to enable further research. We open-source our system at https://github.com/princeton-vl/InfinigenStereo to enable further research on procedural stereo datasets.
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