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
Latest papers
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images
The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data.
CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection
lartpang/caver
•
•
Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration.
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
jiwei0921/jsm
•
•
NeurIPS 2021
As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.
TriTransNet: RGB-D Salient Object Detection with a Triplet Transformer Embedding Network
In view of the more contribution of high-level features for the performance, we propose a triplet transformer embedding module to enhance them by learning long-range dependencies across layers.
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
kingcong/CDINet
•
•
For the cross-modality interaction in feature encoder, existing methods either indiscriminately treat RGB and depth modalities, or only habitually utilize depth cues as auxiliary information of the RGB branch.
Depth Quality-Inspired Feature Manipulation for Efficient RGB-D Salient Object Detection
zwbx/DFM-Net
•
•
To tackle this dilemma and also inspired by the fact that depth quality is a key factor influencing the accuracy, we propose a novel depth quality-inspired feature manipulation (DQFM) process, which is efficient itself and can serve as a gating mechanism for filtering depth features to greatly boost the accuracy.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Visual Saliency Transformer
We also develop a token-based multi-task decoder to simultaneously perform saliency and boundary detection by introducing task-related tokens and a novel patch-task-attention mechanism.
BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection
zwbx/BTS-Net
•
•
Depth information has been proved beneficial in RGB-D salient object detection (SOD).
Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion
sunpeng1996/DSA2F
•
•
CVPR 2021
In principle, the feature modeling scheme is carried out in a depth-sensitive attention module, which leads to the RGB feature enhancement as well as the background distraction reduction by capturing the depth geometry prior.
