Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
Consistent Depth Maps Recovery from a Video Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-view Superpixel Stereo in Urban Environments
International Journal of Computer Vision
Accurate, Dense, and Robust Multiview Stereopsis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents a multi-view stereo algorithm for piecewise planar scene reconstruction and optimization. Our segmentation-based reconstruction algorithm is iterative to minimize our defined energy function, consisting of reconstruction, refinement and optimization steps. The first step is a plane initialization to allow each segment to have a set of initial plane candidates. Then a plane refinement based on non-linear optimization improves the accuracy of the segment planes. Finally a plane optimization with a segment-adjacency graph leads to optimal segment planes, each of which is chosen among possible plane candidates by evaluating its relationship with adjacent planes in 3D. This algorithm yields better accuracy and performance, compared to the previous algorithms described in this paper. The results show our method is suitable for outdoor or aerial urban scene reconstruction, especially in wide baselines and images with textureless regions.