RGB-D Salient Object Detection
56 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
Is Depth Really Necessary for Salient Object Detection?
To solve this, many recent RGBD-based networks are proposed by adopting the depth map as an independent input and fuse the features with RGB information.
Select, Supplement and Focus for RGB-D Saliency Detection
OIPLab-DUT/CVPR_SSF-RGBD
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CVPR 2020
Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction.
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object Detection
OIPLab-DUT/CVPR2020-A2dele
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CVPR 2020
Existing state-of-the-art RGB-D salient object detection methods explore RGB-D data relying on a two-stream architecture, in which an independent subnetwork is required to process depth data.
Learning Selective Self-Mutual Attention for RGB-D Saliency Detection
Considering the reliability of the other modality's attention, we further propose a selection attention to weight the newly added attention term.
Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection
The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information.
RGB-D Salient Object Detection with Cross-Modality Modulation and Selection
Li-Chongyi/cmMS-ECCV20
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ECCV 2020
Second, we propose an adaptive feature selection (AFS) module to select saliency-related features and suppress the inferior ones.
A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection
Xiaoqi-Zhao-DLUT/DANet-RGBD-Saliency
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ECCV 2020
In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion between RGB and depth, which saves the feature encoder of the depth stream and achieves a lightweight and real-time model.
Cascade Graph Neural Networks for RGB-D Salient Object Detection
Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduceCascade Graph Neural Networks(Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.
Depth Quality Aware Salient Object Detection
qdu1995/DQSD
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The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D).
