RGB-D Salient Object Detection
59 papers with code • 8 benchmarks • 5 datasets
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 )
Benchmarks
These leaderboards are used to track progress in RGB-D Salient Object Detection
Libraries
Use these libraries to find RGB-D Salient Object Detection models and implementations
NLPR
LFSD
SIP
NJU2K
ReDWeb-S
Most implemented papers
RGB-D Salient Object Detection: A Survey
Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well.
Uncertainty Inspired RGB-D Saliency Detection
Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.
CIR-Net: Cross-modality Interaction and Refinement for RGB-D Salient Object Detection
Lin-Qinwei/CIR-Net-MindSpore.git
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Focusing on the issue of how to effectively capture and utilize cross-modality information in RGB-D salient object detection (SOD) task, we present a convolutional neural network (CNN) model, named CIR-Net, based on the novel cross-modality interaction and refinement.
PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection
One is the lack of tremendous amount of annotated data to train a network.
Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images.
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
DengPingFan/D3NetBenchmark
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The use of RGB-D information for salient object detection has been extensively explored in recent years.
Bifurcated backbone strategy for RGB-D salient object detection
zyjwuyan/BBS-Net
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In particular, first, we propose to regroup the multi-level features into teacher and student features using a bifurcated backbone strategy (BBS).
Cross-Modal Weighting Network for RGB-D Salient Object Detection
In this paper, we propose a novel Cross-Modal Weighting (CMW) strategy to encourage comprehensive interactions between RGB and depth channels for RGB-D SOD.
Accurate RGB-D Salient Object Detection via Collaborative Learning
OIPLab-DUT/CoNet
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ECCV 2020
The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries.
Siamese Network for RGB-D Salient Object Detection and Beyond
kerenfu/JLDCF
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Inspired by the observation that RGB and depth modalities actually present certain commonality in distinguishing salient objects, a novel joint learning and densely cooperative fusion (JL-DCF) architecture is designed to learn from both RGB and depth inputs through a shared network backbone, known as the Siamese architecture.
