Bridge-UniPS: Bridging Calibrated Photometric Stereo toward Universal Photometric Stereo

Universal photometric stereo (UniPS) aims to recover sur-face normals from images captured under unconstrained illumination,which fundamentally differs from calibrated photometric stereo (CPS),where lighting conditions are known. This gap forces most existing UniPSmethods to adopt fully end-to-end learning that struggles to disentangleillumination and surface geometry due to the lack of explicit physical con-straints. In this paper, we propose Bridge-UniPS, a novel frameworkthat bridges CPS and UniPS through a lighting adapter trained withoutdirect intermediate supervision. The adapter transforms images capturedunder unconstrained illumination into a sequence of image–illuminationpairs directly compatible with CPS models, allowing CPS to act as aphysics-aligned intermediate representation and thereby improving theaccuracy of surface normal recovery. The lighting adapter receives nodirect supervision on Bridge Images or target lighting directions, butis optimized through the final normal-estimation objective with gra-dients propagated through a frozen CPS network. It can still produceCPS-compatible Bridge Images while preserving illumination-geometryconsistency. This behavior suggests that modern learning-based CPSmodels provide useful physical priors to guide the reconstruction. Ex-tensive experiments on multiple benchmarks demonstrate that Bridge-UniPS achieves state-of-the-art performance under unconstrained illu-mination, exhibiting strong generalization and significantly improvingfine-scale surface normal accuracy.