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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
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
2D Car Detection in Radar Data with PointNets—0
Adaptive Fusion for RGB-D Salient Object DetectionCode0
Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object DetectionCode0
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object DetectionCode0
Learning RGB-D Salient Object Detection using background enclosure, depth contrast, and top-down features—0
RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning—0
RGBD Salient Object Detection via Deep Fusion—0
Local Background Enclosure for RGB-D Salient Object Detection—0
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