Binocular Image Flows: Steps Toward Stereo-Motion Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing occluding and transparent motions
International Journal of Computer Vision
The computation of optical flow
ACM Computing Surveys (CSUR)
Procedural elements for computer graphics (2nd ed.)
Procedural elements for computer graphics (2nd ed.)
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Artificial Vision for Mobile Robots: Stereo Vision and Multisensory Perception
Algebraic Functions For Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Based Tracking in an Image Sequence
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A Stereo Machine for Video-Rate Dense Depth Mapping and Its New Applications
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Virtual Worlds using Computer Vision
CVVRHC '98 Proceedings of the 1998 Workshop on Computer Vision for Virtual Reality Based Human Communications (CVVRHC '98)
Electronically directed "focal" stereo
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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New applications in fields such as augmented or virtualized reality have created a demand for dense, accurate real-time stereo reconstruction. Our goal is to reconstruct a user and her office environment for networked tele-immersion, which requires accurate depth values in a relatively large workspace. In order to cope with the combinatorics of stereo correspondence we can exploit the temporal coherence of image sequences by using coarse optical flow estimates to bound disparity search ranges at the next iteration. We use a simple flood fill segmentation method to cluster similar disparity values into overlapping windows and predict their motion over time using a single optical flow calculation per window. We assume that a contiguous region of disparity represents a single smooth surface which allows us to restrict our search to a narrow disparity range. The values in the range may vary over time as objects move nearer or farther away in Z, but we can limit the number of disparities to a feasible search size per window. Further, the disparity search and optical flow calculation are independent for each window, and allow natural distribution over a multi-processor architecture. We have examined the relative complexity of stereo correspondence on full images versus our proposed window system and found that, depending on the number of frames in time used to estimate optical flow, the window-based system requires about half the time of standard correlation stereo. Experimental comparison to full image correspondence search shows our window-based reconstructions compare favourably to those generated by the full algorithm, even after several frames of propagation via estimated optical flow. The result is a system twice as fast as conventional dense correspondence without significant degradation of extracted depth values.