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While deep learning has recently achieved great success on multi-view stereo (MVS), limited training data makes the trained model hard to be generalized to unseen scenarios. Compared with other computer vision tasks, it is rather difficult to collect a large-scale MVS dataset as it requires expensive active scanners and labor-intensive process to obtain ground truth 3D structures. In this paper, we introduce BlendedMVS, a novel large-scale dataset, to provide sufficient training ground truthdoi:10.1109/cvpr42600.2020.00186 dblp:conf/cvpr/0008LLZRZFQ20 fatcat:olhoxgxurffwbiteo5j42jq2m4