123. view synthesis with hierarchical clustering based occlusion filling
Name: Ji Dai
Grad Year: 2020
This paper presents a depth image based rendering algorithm for view synthesis task. We address the challenging occlusion filling problem with a hierarchical clustering approach. Depth distribution of neighboring pixels around each occlusion is explored and numbers of surrounding depth planes are determined with agglomerative clustering. Pixels from the most distant plane are picked as candidates to restore that occlusion. The proposed algorithm is evaluated on Middlebury stereo dataset and Microsoft Research 3D video dataset. Results show that our method ranks among the best performers.
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