Occlusion-aware Bi-directional Guided Network for Light Field Salient Object Detection

Existing light field based works utilize either views or focal stacks for saliency detection. However, since depth information exists implicitly in adjacent views or different focal slices, it is difficult to exploit scene depth information from both. By comparison, Epipolar Plane Images (EPIs) provide explicit accurate scene depth and occlusion information by projected pixel lines. Due to the fact that the depth of an object is often continuous, the distribution of occlusion edges concentrates more on object boundaries compared with traditional color edges, which is more beneficial for improving accuracy and completeness of saliency detection. In this paper, we propose a learning-based network to exploit occlusion features from EPIs and integrate high-level features from the central view for accurate salient object detection. Specifically, a novel Occlusion Extraction Module is proposed to extract occlusion boundary features from horizontal and vertical EPIs. In order to naturally combine occlusion features in EPIs and high-level features in central view, we design a concise Bi-directional Guiding Flow based on cascaded decoders. The flow leverages generated salient edge predictions and salient object predictions to refine features in mutual encoding processes. Experimental results demonstrate that our approach achieves state-of-the-art performance in both segmentation accuracy and edge clarity.