Event-based Photometric Stereo via Rotating Illumination and Per-Pixel Learning

• URL: http://arxiv.org/abs/2603.10748v1
• Date: Wed, 11 Mar 2026 13:22:34 GMT
• Title: Event-based Photometric Stereo via Rotating Illumination and Per-Pixel Learning
• Abstract summary: Photometric stereo is a technique for estimating surface normals using images captured under varying illumination.<n>We propose an event-based photometric stereo system that is effective in scenarios with continuously varying scene radiance and high dynamic range conditions.<n>We introduce a lightweight per-pixel multi-layer neural network that directly predicts surface normals from event signals generated by intensity changes as the light source rotates.
• Score: 39.66238668089539
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Photometric stereo is a technique for estimating surface normals using images captured under varying illumination. However, conventional frame-based photometric stereo methods are limited in real-world applications due to their reliance on controlled lighting, and susceptibility to ambient illumination. To address these limitations, we propose an event-based photometric stereo system that leverages an event camera, which is effective in scenarios with continuously varying scene radiance and high dynamic range conditions. Our setup employs a single light source moving along a predefined circular trajectory, eliminating the need for multiple synchronized light sources and enabling a more compact and scalable design. We further introduce a lightweight per-pixel multi-layer neural network that directly predicts surface normals from event signals generated by intensity changes as the light source rotates, without system calibration. Experimental results on benchmark datasets and real-world data collected with our data acquisition system demonstrate the effectiveness of our method, achieving a 7.12\% reduction in mean angular error compared to existing event-based photometric stereo methods. In addition, our method demonstrates robustness in regions with sparse event activity, strong ambient illumination, and scenes affected by specularities.

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