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Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
We address the problem of depth estimation from lightfield images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach is that we utilize all available data, and not erroneously discard valuable information in
doi:10.1109/cvpr.2018.00476
dblp:conf/cvpr/SchillingDRJ18
fatcat:q6h3ymaxi5hnxktxis5p3sdvce