Paper Detail
Zeyu Ma, Alexander Raistrick, Jia Deng
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.
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@article{ma2026fully,
title = {Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo},
author = {Zeyu Ma and Alexander Raistrick and Jia Deng},
year = {2026},
abstract = {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-},
url = {https://arxiv.org/abs/2604.04925},
keywords = {cs.CV},
eprint = {2604.04925},
archiveprefix = {arXiv},
}
{}