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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 )

Papers

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Showing 21–30 of 88 papers

TitleStatusHype
RGB-D Salient Object Detection via 3D Convolutional Neural NetworksCode1
Learning Selective Mutual Attention and Contrast for RGB-D Saliency DetectionCode1
Uncertainty Inspired RGB-D Saliency DetectionCode1
Siamese Network for RGB-D Salient Object Detection and BeyondCode1
Progressively Guided Alternate Refinement Network for RGB-D Salient Object DetectionCode1
Depth Quality Aware Salient Object DetectionCode1
Cascade Graph Neural Networks for RGB-D Salient Object DetectionCode1
RGB-D Salient Object Detection: A SurveyCode1
BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy NetworkCode1
Accurate RGB-D Salient Object Detection via Collaborative LearningCode1
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