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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 41–50 of 88 papers

TitleStatusHype
Hierarchical Dynamic Filtering Network for RGB-D Salient Object DetectionCode1
Uncertainty Inspired RGB-D Saliency DetectionCode1
Is Depth Really Necessary for Salient Object Detection?Code1
Joint Learning of Salient Object Detection, Depth Estimation and Contour ExtractionCode1
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object DetectionCode1
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection—0
ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection—0
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network—0
A Unified Structure for Efficient RGB and RGB-D Salient Object Detection—0
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse—0
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