Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ACM SIGGRAPH 2007 papers
Stereo Processing by Semiglobal Matching and Mutual Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of stereo vision and Time-Of-Flight imaging for improved 3D estimation
International Journal of Intelligent Systems Technologies and Applications
A Novel Interpolation Scheme for Range Data with Side Information
CVMP '09 Proceedings of the 2009 Conference for Visual Media Production
Reliability Fusion of Time-of-Flight Depth and Stereo Geometry for High Quality Depth Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Depth estimation for dynamic scenes is a challenging and relevant problem in computer vision. Although this problem can be tackled by means of ToF cameras or stereo vision systems, each of the two systems alone has its own limitations. In this paper a framework for the fusion of 3D data produced by a ToF camera and a stereo vision system is proposed. Initially, depth data acquired by the ToF camera are up-sampled to the spatial resolution of the stereo vision images by a novel up-sampling algorithm based on image segmentation and bilateral filtering. In parallel a dense disparity field is obtained by a stereo vision algorithm. Finally, the up-sampled ToF depth data and the disparity field provided by stereo vision are synergically fused by enforcing the local consistency of depth data. The depth information obtained with the proposed framework is characterized by the high resolution of the stereo vision system and by an improved accuracy with respect to the one produced by both subsystems. Experimental results clearly show how the proposed method is able to outperform the compared fusion algorithms.