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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 71–80 of 88 papers

TitleStatusHype
Bilateral Attention Network for RGB-D Salient Object DetectionCode1
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
cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial NetworksCode0
Depth-Induced Multi-Scale Recurrent Attention Network for Saliency DetectionCode0
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse—0
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale BenchmarksCode1
Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object DetectionCode0
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