Camera field rendering for static and dynamic scenes
Graphical Models
Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimate Large Motions Using the Reliability-Based Motion Estimation Algorithm
International Journal of Computer Vision
Stabilizing Stereo Correspondence Computation Using Delaunay Triangulation and Planar Homography
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Improving Border Localization of Multi-Baseline Stereo Using Border-Cut
International Journal of Computer Vision
Image and Vision Computing
Spatial and temporal up-conversion technique for depth video
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Hierarchical stereo matching: from foreground to background
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Robust 3D human face reconstruction by consumer binocular-stereo cameras
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
Iterative semi-global matching for robust driver assistance systems
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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A method for solving binocular and multi-view stereomatching problems is presented in this paper. A weakconsistency constraint is proposed, which expresses thevisibility constraint in the image space. It can be provedthat the weak consistency constraint holds for scenes thatcan be represented by a set of 3D points. As well, alsoproposed is a new reliability measure for dynamicprogramming techniques, which evaluates the reliability ofa given match. A novel reliability-based dynamicprogramming algorithm is derived accordingly, which canselectively assign disparity values to pixels when thereliabilities of the corresponding matches exceed a giventhreshold. Consistency constraints and the new reliability-baseddynamic programming algorithm can be combinedin an iterative approach. The experimental results showthat the iterative approach can produce dense (60~90%)and reliable (total error rate of 0.1~1.1%) matching forbinocular stereo datasets. It can also generate promisingdisparity maps for trinocular and multi-view stereodatasets.