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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 51–60 of 88 papers

TitleStatusHype
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion—0
Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection—0
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection—0
Dynamic Message Propagation Network for RGB-D Salient Object Detection—0
GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection—0
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness—0
Hierarchical Cross-modal Transformer for RGB-D Salient Object Detection—0
HODINet: High-Order Discrepant Interaction Network for RGB-D Salient Object Detection—0
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction—0
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features—0
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