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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
Local Background Enclosure for RGB-D Salient Object Detection—0
M2RNet: Multi-modal and Multi-scale Refined Network for RGB-D Salient Object Detection—0
Middle-level Fusion for Lightweight RGB-D Salient Object Detection—0
Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection—0
Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection—0
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection—0
Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection—0
RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning—0
RGBD Salient Object Detection via Deep Fusion—0
RGB-D Salient Object Detection with Ubiquitous Target Awareness—0
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