Parallax-Aware Network for Light Field Salient Object Detection
Bo Yuan, Yao Jiang, Keren Fu, Qijun Zhao
Abstract
Multi-view images capture scene details from different views, making them advantageous for light field salient object detection (LF SOD). However, most existing LF SOD methods neglect effective modeling and utilization of parallax information inherent in multi-view images. To address this limitation, we propose to explicitly model parallax information and conduct SOD in a parallax-aware manner, resulting in a novel network called PANet. Our model initiates by generating horizontal and vertical visual parallax maps from four border views using optical flow estimation. We then introduce a parallax-aware network, incorporating a parallax processing module (PPM) that handles both parallax quality assessment and parallax correction. In the parallax correction phase, we design a channel-based correction unit (CCU) and a graph-based correction unit (GCU) to rectify deviations of parallax features in a direction-specific manner. Additionally, a parallax supplement module (PSM) seamlessly fuses the parallax information from different directions and embeds it into the center view, thereby improving SOD accuracy. Experiments on three benchmark datasets demonstrate the superiority of our PANet model over 15 state-of-the-art models. Our code for the model will be publicly available soon.