Title

Training Many-Parameter Shape-From-Shading Models Using A Surface Database

Abstract

Shape-from-shading (SFS) methods tend to rely on models with few parameters because these parameters need to be hand-tuned. This limits the number of different cues that the SFS problem can exploit. In this paper, we show how machine learning can be applied to an SFS model with a large number of parameters. Our system learns a set of weighting parameters that use the intensity of each pixel in the image to gauge the importance of that pixel in the shape reconstruction process. We show empirically that this leads to a significant increase in the accuracy of the recovered surfaces. Our learning approach is novel in that the parameters are optimized with respect to actual surface output by the system. In the first, offline phase, a hemisphere is rendered using a known illumination direction. The isophotes in the resulting reflectance map are then modelled using Gaussian mixtures to obtain a parametric representation of the isophotes. This Gaussian parameterization is then used in the second phase to learn intensity-based weights using a database of 3D shapes. The weights can also be optimized for a particular input image. ©2009 IEEE.

Publication Date

12-1-2009

Publication Title

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Number of Pages

1433-1440

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICCVW.2009.5457444

Socpus ID

77953184892 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953184892

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