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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 76–88 of 88 papers

TitleStatusHype
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness—0
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection—0
GroupTransNet: Group Transformer Network for RGB-D Salient Object Detection—0
Dynamic Message Propagation Network for RGB-D Salient Object Detection—0
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection—0
SiaTrans: Siamese Transformer Network for RGB-D Salient Object Detection with Depth Image Classification—0
Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection—0
ACFNet: Adaptively-Cooperative Fusion Network for RGB-D Salient Object Detection—0
Decomposed Guided Dynamic Filters for Efficient RGB-Guided Depth Completion—0
CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse—0
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection—0
Middle-level Fusion for Lightweight RGB-D Salient Object Detection—0
Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection—0
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