DPPNet: A Depth Pixel-Wise Potential-Aware Network for RGB-D Salient Object Detection
Junbin Yuan, Yiqi Wang, Zhoutao Wang, Qingzhen Xu, Bharadwaj Veeravalli, Xulei Yang · IEEE Transactions on Multimedia · 2025
Depth cues are essential for visual perception tasks like Salient Object Detection (SOD). Due to varying depth reliability across scenes, some researchers propose evaluating the overall quality of the depth maps and discarding the less reliable ones to avoid contamination. However, these methods often fail to fully utilize valuable information in depth maps, leading to sub-optimal performance particularly when depth quality is unreliable. Since low-quality depth maps still contain useful information that potentially improves model performance, we propose a Depth Pixel-wise Potential-aware Network to leverage these depth cues effectively. This network includes two novel components designed: 1) A learning strategy for explicitly modeling the confidence of each depth pixel to assist the model in locating valid information in the depth map. 2) A cross-modal adaptive multiple fusion module that fuses features from both RGB and depth modalities. It aims to mitigate the contamination effect of unreliable depth maps and fully exploit the benefits of multiple fusion strategies. Experimental results show that on four publicly available datasets, our method outperforms 17 mainstream methods on various evaluation metrics.