Surface normal estimation from optimized and distributed light sources using DNN-based photometric stereo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Photometric stereo (PS) is a major technique to recover surface normal for each pixel. However, since it assumes Lambertian surface and directional light to estimate the value, a large number of images are usually required to avoid the effects of outliers and noise. In this paper, we propose a technique to reduce the number of images by using distributed light sources, where the patterns are optimized by a deep neural network (DNN). In addition, to efficiently realize the distributed light, we use an optical diffuser with a video projector, where the diffuser is illuminated by the projector from behind, the illuminated area on the diffuser works as if an arbitrary-shaped area light. To estimate the surface normal using the distributed light source, we propose a near-light photometric stereo (NLPS) using DNN. Since optimization of the pattern of distributed light is achieved by a differentiable renderer, it is connected with NLPS network, achieving end-to-end learning. The experiments are conducted to show the successful estimation of the surface normal by our method from a small number of images.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages311-320
Number of pages10
ISBN (Electronic)9781665493468
DOIs
Publication statusPublished - 2023
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 - Waikoloa, United States
Duration: Jan 3 2023Jan 7 2023

Publication series

NameProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

Conference

Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
Country/TerritoryUnited States
CityWaikoloa
Period1/3/231/7/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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