Progressive Multi-Plane Images Construction for Light Field Occlusion Removal

Recently, Light Field (LF) shows great potential in removing occlusion since the objects occluded in some views may be visible in other views. However, existing LF-based methods implicitly model each scene and can only remove objects that have positive disparities in one central views. In this paper, we propose a novel Progressive Multi-Plane Images (MPI) Construction method specifically designed for LF-based occlusion removal. Different from the previous MPI construction methods, we progressively construct MPIs layer by layer in order from near to far. In order to accurately model the current layer, the positions of foreground occlusions in the nearer layers are taken as occlusion prior. Specifically, we propose an Occlusion-Aware Attention Network to generate each layer of MPIs with reliable information in occluded regions. For each layer, occlusions in the current layer are filtered out so that the background is better recovered just using the visible views instead of the other occluded views. Then, by simply removing the layers containing occlusions and rendering MPIs in kinds of viewpoints, the occlusion removal results for different views are generated. Experiments on synthetic and real-world scenes show that our method outperforms state-of-the-art LF occlusion removal methods in quantitative and visual comparisons. Moreover, we also apply the proposed progressive MPI construction method to the view synthesis task. The occlusion edges in our synthesized views achieve significantly better quality, which also verifies that our method can better model the occluded regions.