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Factor graph methods for three-dimensional shape reconstruction as applied to LIDAR imaging
Author(s) -
Robert Drost,
Andrew C. Singer
Publication year - 2004
Publication title -
journal of the optical society of america a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.803
H-Index - 158
eISSN - 1520-8532
pISSN - 1084-7529
DOI - 10.1364/josaa.21.001855
Subject(s) - silhouette , artificial intelligence , computer vision , computer science , lidar , factor graph , voxel , cut , segmentation , iterative reconstruction , graph , 3d reconstruction , pattern recognition (psychology) , image segmentation , remote sensing , algorithm , geology , theoretical computer science , decoding methods
Two methods based on factor graphs for reconstructing the three-dimensional (3D) shape of an object from a series of two-dimensional images are presented. First, a factor graph model is developed for image segmentation to obtain silhouettes from raw images; the shape-from-silhouette technique is then applied to yield the 3D reconstruction of the object. The second method presented is a direct 3D reconstruction of the object using a factor graph model for the voxels of the reconstruction. While both methods should be applicable to a variety of input data types, they will be developed and demonstrated for a particular application involving the LIDAR imaging of a submerged target. Results from simulations and from real LIDAR data are shown that detail the performance of the methods.

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