Sparse Views Near Light: A Practical Paradigm for Uncalibrated Point-light Photometric Stereo

Mohammed Brahimi, Bjoern Haefner, Zhenzhang Ye, Bastian Goldluecke, Daniel Cremers; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 11862-11872

Neural approaches have shown a significant progress on camera-based reconstruction. But they require either a fairly dense sampling of the viewing sphere or pre-training on an existing dataset thereby limiting their generalizability. In contrast photometric stereo (PS) approaches have shown great potential for achieving high-quality reconstruction under sparse viewpoints. Yet they are impractical because they typically require tedious laboratory conditions are restricted to dark rooms and often multi-staged making them subject to accumulated errors. To address these shortcomings we propose an end-to-end uncalibrated multi-view PS framework for reconstructing high-resolution shapes acquired from sparse viewpoints in a real-world environment. We relax the dark room assumption and allow a combination of static ambient lighting and dynamic near LED lighting thereby enabling easy data capture outside the lab. Experimental validation confirms that it outperforms existing baseline approaches in the regime of sparse viewpoints by a large margin. This allows to bring high accuracy 3D reconstruction from the dark room to the real world while maintaining a reasonable data capture complexity.