L-DPSNet: Deep Photometric Stereo Network via Local Diffuse Reflectance Maxima

Kanghui Zeng, Chao Xu, Jing Hu*, Yushi Li, Zhaopeng Meng

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Reconstructing surface normal from the reflectance observations of real objects is a challenging issue. Although recent works on photometric stereo exploit various reflectance-normal mapping models, none of them take both illumination and LDR maximum into account. In this paper, we combine a fusion learning network with LDR maxima to recover the normal of the underlying surface. Unlike traditional formalization, the initial normal estimated by solving the generalized bas-relief (GBR) ambiguity is employed to promote the performance of our learning framework. As an uncalibrated photometric stereo network, our method, called L-DPSNet, takes advantage of LDR-derived information in normal prediction. We present the qualitative and quantitative experiments implemented using synthetic and real data to demonstrate the effectiveness of the proposed model.

Original languageEnglish
Title of host publicationNeural Information Processing - 28th International Conference, ICONIP 2021, Proceedings
EditorsTeddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9783030923068
Publication statusPublished - 2021
Externally publishedYes
Event28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online
Duration: 8 Dec 202112 Dec 2021

Publication series

NameCommunications in Computer and Information Science
Volume1516 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference28th International Conference on Neural Information Processing, ICONIP 2021
CityVirtual, Online


  • Deep neural network
  • Diffuse reflectance maxima
  • Uncalibrated photometric stereo

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