SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo

• URL: http://arxiv.org/abs/2604.04925v2
• Date: Tue, 07 Apr 2026 18:40:28 GMT
• Title: SimpleProc: Fully Procedural Synthetic Data from Simple Rules for Multi-View Stereo
• Abstract summary: We show that we can generate effective training data using SimpleProc: a new, fully procedural generator driven by a very small set of rules.<n>At a modest scale of 8,000 images, our approach achieves superior results compared to manually curated images from games and real-world objects.<n>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.
• Score: 59.11389699606293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-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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