RGB-D Salient Object Detection

51 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

RGB-D Salient Object Detection: A Survey

taozh2017/RGBD-SODsurvey 1 Aug 2020

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

JingZhang617/UCNet 7 Sep 2020

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.

PDNet: Prior-model Guided Depth-enhanced Network for Salient Object Detection

ChunbiaoZhu/PDNet 23 Mar 2018

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

taozh2017/RGBD-SODsurvey CVPR 2019

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 15 Jul 2019

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 6 Jul 2020

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

MathLee/CMWNet ECCV 2020

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 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 26 Aug 2020

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.

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.