SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910109952638976 |
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| author | Ma, Zeyu Raistrick, Alexander Deng, Jia |
| author_facet | Ma, Zeyu Raistrick, Alexander Deng, Jia |
| contents | In this paper, we explore the design space of procedural rules for multi-view stereo (MVS). We demonstrate that we can generate effective training data using SimpleProc: a new, fully procedural generator driven by a very small set of rules using Non-Uniform Rational Basis Splines (NURBS), as well as basic displacement and texture patterns. At a modest scale of 8,000 images, our approach achieves superior results compared to manually curated images (at the same scale) sourced from games and real-world objects. When scaled to 352,000 images, our method yields performance comparable to--and in several benchmarks, exceeding--models trained on over 692,000 manually curated images. The source code and the data are available at https://github.com/princeton-vl/SimpleProc. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_04925 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo Ma, Zeyu Raistrick, Alexander Deng, Jia Computer Vision and Pattern Recognition In this paper, we explore the design space of procedural rules for multi-view stereo (MVS). We demonstrate that we can generate effective training data using SimpleProc: a new, fully procedural generator driven by a very small set of rules using Non-Uniform Rational Basis Splines (NURBS), as well as basic displacement and texture patterns. At a modest scale of 8,000 images, our approach achieves superior results compared to manually curated images (at the same scale) sourced from games and real-world objects. When scaled to 352,000 images, our method yields performance comparable to--and in several benchmarks, exceeding--models trained on over 692,000 manually curated images. The source code and the data are available at https://github.com/princeton-vl/SimpleProc. |
| title | SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.04925 |