RGB-D Salient Object Detection

RGB-D Salient object detection (SOD) aims at distinguishing the most visually distinctive objects or regions in a scene from the given RGB and Depth data. It has a wide range of applications, including video/image segmentation, object recognition, visual tracking, foreground maps evaluation, image retrieval, content-aware image editing, information discovery, photosynthesis, and weakly supervised semantic segmentation. Here, depth information plays an important complementary role in finding salient objects. Online benchmark: http://dpfan.net/d3netbenchmark. ( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )

RGB-D Salient Object Detection

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

RGB-D salient object detection: A survey

Content

Uncertainty Inspired RGB-D Saliency Detection

CIR-Net: Cross-Modality Interaction and Refinement for RGB-D Salient Object Detection

PDNet: Prior-Model Guided Depth-Enhanced Network for Salient Object Detection

Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection

Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks

Bifurcated Backbone Strategy for RGB-D Salient Object Detection

Cross-Modal Weighting Network for RGB-D Salient Object Detection

Accurate RGB-D Salient Object Detection via Collaborative Learning

Siamese Network for RGB-D Salient Object Detection and Beyond