Deep Near-Light Photometric Stereo for Spatially Varying Reflectances

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)


This paper presents a near-light photometric stereo method for spatially varying reflectances. Recent studies in photometric stereo proposed learning-based approaches to handle diverse real-world reflectances and achieve high accuracy compared to conventional methods. However, they assume distant (i.e., parallel) lights, which can in practical settings only be approximately realized, and they fail in near-light conditions. Near-light photometric stereo methods address near-light conditions but previous works are limited to over-simplified reflectances, such as Lambertian reflectance. The proposed method takes a hybrid approach of distant- and near-light models, where the surface normal of a small area (corresponding to a pixel) is computed locally with a distant light assumption, and the reconstruction error is assessed based on a near-light image formation model. This paper is the first work to solve unknown, spatially varying, diverse reflectances in near-light photometric stereo.


Near-light photometric stereo 



This work was supported by JSPS KAKENHI Grant Number JP19H01123. Hiroaki Santo and Michael Waechter are grateful for support through a JSPS Research Fellowship for Young Scientists (JP19J10326) and JSPS Postdoctoral Fellowship (JP17F17350), respectively.

Supplementary material

504445_1_En_9_MOESM1_ESM.pdf (17.3 mb)
Supplementary material 1 (pdf 17708 KB)


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Graduate School of Information Science and TechnologyOsaka UniversityOsakaJapan

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