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3-D Scene Data Recovery using Omnidirectional Multibaseline Stereo

Sing Bing Kang and Richard Szeliski


A traditional approach to extracting geometric information from a large scene is to compute multiple 3-D depth maps from stereo pairs or direct range finders, and then to merge the 3-D data. However, the resulting merged depth maps may be subject to merging errors if the relative poses between depth maps are not known exactly. In addition, the 3-D data may also have to be resampled before merging, which adds additional complexity and potential sources of errors. This paper provides a means of directly extracting 3-D data covering a very widefield ofview, thus by-passing the needfor numerous depth map merging. In our work, cylindrical images are first composited from sequences of images taken while the camera is rotated 360 about a vertical axis. By taking such image panoramas at different camera locations, we can recover 3-D data ofthe scene using a set ofsimple techniques: feature tracking, an 8-point structure from motion algorithm, and multibaseline stereo. We also investigate the effect ofmedianfiltering on the recovered 3D point distributions, and show the results ofour approach applied to both synthetic and real scenes.


Publication typeInproceedings
Published inIEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'96)
AddressSan Francisco
PublisherIEEE Computer Society
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