Skip to Main content Skip to Navigation
Conference papers

Optimised photometric stereo via non-convex variational minimisation

Abstract : Estimating the shape and appearance of a three dimensional object from flat images is a challenging research topic that is still actively pursued. Among the various techniques available, Photometric Stereo is known to provide very accurate local shape recovery, in terms of surface normals. In this work, we propose to minimise non-convex variational models for Photometric Stereo that recover the depth information directly. We suggest an approach based on a novel optimisation scheme for non-convex cost functions. Experiments show that our strategy achieves more accurate results than competing approaches.
Keywords : Photometric stereo
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download
Contributor : Open Archive Toulouse Archive Ouverte (OATAO) Connect in order to contact the contributor
Submitted on : Tuesday, January 24, 2017 - 4:10:24 PM
Last modification on : Wednesday, June 1, 2022 - 4:08:34 AM
Long-term archiving on: : Tuesday, April 25, 2017 - 5:33:03 PM


Files produced by the author(s)


  • HAL Id : hal-01445135, version 1
  • OATAO : 17234


Laurent Hoeltgen, yvain Quéau, Michael Breuss, Georg Radow. Optimised photometric stereo via non-convex variational minimisation. 27th British Machine Vision Conference (BMVC 2016), Sep 2016, York, United Kingdom. pp. 1. ⟨hal-01445135⟩



Record views


Files downloads