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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 61–70 of 88 papers

TitleStatusHype
TANet: Transformer-based Asymmetric Network for RGB-D Salient Object DetectionCode0
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection—0
M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection—0
A Unified Structure for Efficient RGB and RGB-D Salient Object Detection—0
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network—0
2D Car Detection in Radar Data with PointNets—0
Local Background Enclosure for RGB-D Salient Object Detection—0
Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection—0
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features—0
Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction—0
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