Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Visual Modeling with a Hand-Held Camera
International Journal of Computer Vision
3D Reconstruction by Fitting Low-Rank Matrices with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Linear Time Histogram Metric for Improved SIFT Matching
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Make3D: Learning 3D Scene Structure from a Single Still Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Consistent Depth Maps Recovery from a Video Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering Occlusion Boundaries from an Image
International Journal of Computer Vision
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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In this paper, we propose a novel approach to reconstructing depth map from a video sequence, which not only considers geometry coherence but also temporal coherence. Most of the previous methods of reconstructing depth map from video are based on the assumption of rigid motion, thus they cannot provide satisfactory depth estimation for regions with moving objects. In this work, we develop a depth estimation algorithm that detects regions of moving objects and recover the depth map in a Markov Random Field framework. We first apply SIFT matching across frames in the video sequence and compute the camera parameters for all frames and the 3D positions of the SIFT feature points via structure from motion. Then, the 3D depths at these SIFT points are propagated to the whole image based on image over-segmentation to construct an initial depth map. Then the depth values for the segments with large reprojection errors are refined by minimizing the corresponding re-projection errors. In addition, we detect the area of moving objects from the remaining pixels with large re-projection errors. In the final step, we optimize the depth map estimation in a Markov random filed framework. Some experimental results are shown to demonstrate improved depth estimation results of the proposed algorithm.