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Reconstructing a 3-D depth map from one or more images

โœ Scribed by Min Shao; Rama Chellappa; Tal Simchony


Publisher
Elsevier Science
Year
1991
Weight
725 KB
Volume
53
Category
Article
ISSN
1049-9660

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โœฆ Synopsis


Several algorithms are suggested for recovering depth and orientation maps of a surface from its image intensities. They combine the advantages of stereo vision and shape-from-shading (SFS) methods. These algorithms generate accurate, unambiguous and dense surface depth and orientation maps. Most of the existing SFS algorithms cannot be directly extended to combine stereo images because the recovery of surface depth and that of orientation are separated in these formulations. We first present an SFS algorithm that couples the generation of depth and orientation maps. This formulation also ensures that the reconstructed surface depth and its orientation are consistent. The SFS algorithm for a single image is then extended to utilize stereo images. The correspondence over stereo images is established simultaneously with the generation of surface depth and orientation. An alternative approach is also suggested for combining stereo and SFS techniques. This approach can be used to combine needle maps which are directly available from other sources such as photometric stereo. Finally we present an algorithm to combine sparse depth measurements with an orientation map to reconstruct a surface. The same algorithm can be combined with the above algorithms for solving the SFS problem with sparse depth measurements. Thus various information sources can be used to accurately reconstruct a surface.


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A neural network for recovering 3D shape
โœ Mohamad Ivan Fanany; Itsuo Kumazawa ๐Ÿ“‚ Article ๐Ÿ“… 2004 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 770 KB

In this paper, we present a new neural network (NN) for three-dimensional (3D) shape reconstruction. This NN provides an analytic mapping of an initial 3D polyhedral model into its projection depth images. Through this analytic mapping, the NN can analytically refine vertices position of the model u