Multi-Prior Driven Network for RGB-D Salient Object Detection

Most existing RGB-D salient object detection (SOD) methods rely on high-quality depth images. However, their performance is limited when processing low-quality depth maps. This paper exploits more complementary image priors to guide the model to learn on variable depth maps, and a novel multi-prior driven network called MPDNet is proposed for RGB-D SOD. MPDNet utilizes four processing pipelines to process RGB images and other priors, which include an RGB image processing pipeline, a depth map processing pipeline, a fine-grained and gradient prior processing pipeline, and an edge learning pipeline. Specifically, fine-grained and gradient priors are input to the same processing pipeline. For the depth maps, fine-grained and gradient priors, a prior channel attention module utilizes the channel attention mechanism to filter noises and highlights the salient cues. The RGB image processing pipeline uses a multi-feature progressive enhancement module to fuse and enhance features from depth maps. And a multi-feature prediction decoder decodes initial salient masks. In the edge learning pipeline, edge prior serves as an edge label and is captured by an edge capture module. Finally, the clear salient masks are obtained by fusing the salient information from the four pipelines. The experimental results on six benchmarks indicate that the proposed method outperforms thirteen state-of-the-art methods in six evaluation metrics.