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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 11–20 of 88 papers

TitleStatusHype
Bilateral Attention Network for RGB-D Salient Object DetectionCode1
An Energy-Based Prior for Generative SaliencyCode1
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object DetectionCode1
Calibrated RGB-D Salient Object DetectionCode1
DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object DetectionCode1
A Single Stream Network for Robust and Real-time RGB-D Salient Object DetectionCode1
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object DetectionCode1
CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object DetectionCode1
Bifurcated backbone strategy for RGB-D salient object detectionCode1
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object DetectionCode1
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