Skip to Main content Skip to Navigation
Conference papers

Towards Robust and Physically Plausible Shaded Stereoscopic Segmentation

Dejun Wang 1 Emmanuel Prados 2, 3 Stefano Soatto 1
3 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : We address the multi-view shape from shading problem, that is the recovery of 3-D shape, lighting configuration and surface albedo from multiple calibrated views. Previous approaches to this problem relied on physically impossible illumination models (negative light) and only work on constant albedo and resulted in biased estimates of shape and lighting positions. Furthermore, since the solution involves infinite-dimensional optimization, existing approaches were quite slow. We develop a new model that explicitly enforces positivity in the light sources with the assumption that the object is Lambertian and its albedo is piecewise constant and show that the new model significantly improves the accuracy and robustness relative to existing approaches. Furthermore, we show that the most computationally expensive step in the optimization can actually be solved in closed form. This significantly improves speed of convergence over existing schemes. We illustrate the behavior of our algorithm directly on the same data used by previous authors, so direct comparison is possible.
Document type :
Conference papers
Complete list of metadatas

Cited literature [5 references]  Display  Hide  Download
Contributor : Emmanuel Prados <>
Submitted on : Tuesday, April 21, 2009 - 5:17:15 PM
Last modification on : Monday, December 28, 2020 - 3:44:02 PM
Long-term archiving on: : Thursday, June 10, 2010 - 7:12:12 PM


Files produced by the author(s)





Dejun Wang, Emmanuel Prados, Stefano Soatto. Towards Robust and Physically Plausible Shaded Stereoscopic Segmentation. Computer Vision and Pattern Recognition Workshop (CVPRW), Jun 2006, New York, United States. ⟨10.1109/CVPRW.2006.204⟩. ⟨inria-00377430⟩



Record views


Files downloads