Anderson, R and Stenger, B and Cipolla, R (2011) Color photometric stereo for multicolored surfaces. Proceedings of the IEEE International Conference on Computer Vision. pp. 2182-2189.Full text not available from this repository.
We present a multispectral photometric stereo method for capturing geometry of deforming surfaces. A novel photometric calibration technique allows calibration of scenes containing multiple piecewise constant chromaticities. This method estimates per-pixel photometric properties, then uses a RANSAC-based approach to estimate the dominant chromaticities in the scene. A likelihood term is developed linking surface normal, image intensity and photometric properties, which allows estimating the number of chromaticities present in a scene to be framed as a model estimation problem. The Bayesian Information Criterion is applied to automatically estimate the number of chromaticities present during calibration. A two-camera stereo system provides low resolution geometry, allowing the likelihood term to be used in segmenting new images into regions of constant chromaticity. This segmentation is carried out in a Markov Random Field framework and allows the correct photometric properties to be used at each pixel to estimate a dense normal map. Results are shown on several challenging real-world sequences, demonstrating state-of-the-art results using only two cameras and three light sources. Quantitative evaluation is provided against synthetic ground truth data. © 2011 IEEE.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:56|
|Last Modified:||26 Apr 2017 01:44|