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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
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object DetectionCode1
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational AutoencodersCode1
Density Map Guided Object Detection in Aerial ImagesCode1
DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object DetectionCode1
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale BenchmarksCode1
Dual Mutual Learning Network with Global-local Awareness for RGB-D Salient Object DetectionCode0
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network—0
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion—0
Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object DetectionCode0
HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection—0
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