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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 31–40 of 88 papers

TitleStatusHype
RGB-D Salient Object Detection with Cross-Modality Modulation and SelectionCode1
A Single Stream Network for Robust and Real-time RGB-D Salient Object DetectionCode1
Hierarchical Dynamic Filtering Network for RGB-D Salient Object DetectionCode1
Cross-Modal Weighting Network for RGB-D Salient Object DetectionCode1
Bifurcated backbone strategy for RGB-D salient object detectionCode1
Select, Supplement and Focus for RGB-D Saliency DetectionCode1
Learning Selective Self-Mutual Attention for RGB-D Saliency DetectionCode1
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object DetectionCode1
Is Depth Really Necessary for Salient Object Detection?Code1
Bilateral Attention Network for RGB-D Salient Object DetectionCode1
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