View synthesis with hierarchical clustering based occlusion filling
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 from which we determine the number of surrounding depth planes with agglomerative clustering. Pixels in 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.