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In this study, the authors propose a multi-view stereo reconstruction method which creates a three-dimensional point cloud of a scene from multiple calibrated images captured from different viewpoints. The method is based on a prioritised match expansion technique, which starts from a sparse set of seed points, and iteratively expands them into neighbouring areas by using multiple expansion stages...
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...
This paper proposes a method for computing a quasi-dense set of matching points between three views of a scene. The method takes a sparse set of seed matches between pairs of views as input and then propagates the seeds to neighboring regions. The proposed method is based on the best-first match propagation strategy, which is here extended from two-view matching to the case of three views. The results...
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