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This paper proposes a prioritized matching approach for finding corresponding points in multiple calibrated images for multi-view stereo reconstruction. The approach takes a sparse set of seed matches between pairs of views as input and then propagates the seeds to neighboring regions by using a prioritized matching method which expands the most promising seeds first. The output of the method is a...
Patch cloud based multi-view stereo methods have proven to be an accurate and scalable approach for scene reconstruction. Their applicability, however, is limited due to the semi-dense nature of their reconstruction. We propose a method to generate a dense depth map from a patch cloud by assuming a planar surface model for non-reconstructed areas. We use local evidence to estimate the best fitting...
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