Geometry Meets Light: Leveraging Geometric Priors for Universal Photometric Stereo under Limited Multi-Illumination Cues

• URL: http://arxiv.org/abs/2511.13015v2
• Date: Tue, 18 Nov 2025 10:05:51 GMT
• Title: Geometry Meets Light: Leveraging Geometric Priors for Universal Photometric Stereo under Limited Multi-Illumination Cues
• Authors: King-Man Tam, Satoshi Ikehata, Yuta Asano, Zhaoyi An, Rei Kawakami,
• Abstract summary: GeoUniPS is a universal photometric stereo network that integrates synthetic supervision with high-level geometric priors.<n>We introduce the PS-Perp dataset with realistic perspective projection to enable learning of spatially varying view directions.
• Score: 24.171649472398514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Universal Photometric Stereo is a promising approach for recovering surface normals without strict lighting assumptions. However, it struggles when multi-illumination cues are unreliable, such as under biased lighting or in shadows or self-occluded regions of complex in-the-wild scenes. We propose GeoUniPS, a universal photometric stereo network that integrates synthetic supervision with high-level geometric priors from large-scale 3D reconstruction models pretrained on massive in-the-wild data. Our key insight is that these 3D reconstruction models serve as visual-geometry foundation models, inherently encoding rich geometric knowledge of real scenes. To leverage this, we design a Light-Geometry Dual-Branch Encoder that extracts both multi-illumination cues and geometric priors from the frozen 3D reconstruction model. We also address the limitations of the conventional orthographic projection assumption by introducing the PS-Perp dataset with realistic perspective projection to enable learning of spatially varying view directions. Extensive experiments demonstrate that GeoUniPS delivers state-of-the-arts performance across multiple datasets, both quantitatively and qualitatively, especially in the complex in-the-wild scenes.

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