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 )

Libraries

Use these libraries to find RGB-D Salient Object Detection models and implementations

Most implemented papers

Point-aware Interaction and CNN-induced Refinement Network for RGB-D Salient Object Detection

big-feather/PICR-Net_ACMMM23_MS 17 Aug 2023

By integrating complementary information from RGB image and depth map, the ability of salient object detection (SOD) for complex and challenging scenes can be improved.

Progressively Complementarity-Aware Fusion Network for RGB-D Salient Object Detection

haochen593/PCA-Fuse_RGBD_CVPR18 CVPR 2018

In this paper, we answer this question from two perspectives: (1) We argue that if the complementary part can be modelled more explicitly, the cross-modal complement is likely to be better captured.

Adaptive Fusion for RGB-D Salient Object Detection

Lucia-Ningning/Adaptive_Fusion_RGBD_Saliency_Detection 5 Jan 2019

RGB-D salient object detection aims to identify the most visually distinctive objects in a pair of color and depth images.

Depth-Induced Multi-Scale Recurrent Attention Network for Saliency Detection

jiwei0921/DMRA ICCV 2019

In this work, we propose a novel depth-induced multi-scale recurrent attention network for saliency detection.

\emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks

wangxiao5791509/cmSalGAN_PyTorch 21 Dec 2019

Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem.

DPANet: Depth Potentiality-Aware Gated Attention Network for RGB-D Salient Object Detection

JosephChenHub/DPANet 19 Mar 2020

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data; (2) how to prevent the contamination effect from the unreliable depth map.

Density Map Guided Object Detection in Aerial Images

Cli98/DMNet 12 Apr 2020

Specifically, we propose a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.

UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders

JingZhang617/UCNet CVPR 2020

In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.

JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection

kerenfu/JLDCF CVPR 2020

This paper proposes a novel joint learning and densely-cooperative fusion (JL-DCF) architecture for RGB-D salient object detection.

Bilateral Attention Network for RGB-D Salient Object Detection

zzhanghub/bianet 30 Apr 2020

To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task.