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RGB-D Salient Object Detection

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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.

<span style="color:grey; opacity: 0.6">( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )</span>

논문

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RGB-D Salient Object Detection

논문

Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection

Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection

Visual Saliency Transformer

Middle-level Fusion for Lightweight RGB-D Salient Object Detection

BTS-Net: Bi-directional Transfer-and-Selection Network For RGB-D Salient Object Detection

Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion

Densely Deformable Efficient Salient Object Detection Network

Self-Supervised Pretraining for RGB-D Salient Object Detection

RGB-D Salient Object Detection via 3D Convolutional Neural Networks

MobileSal: Extremely Efficient RGB-D Salient Object Detection

A Unified Structure for Efficient RGB and RGB-D Salient Object Detection

Learning Selective Mutual Attention and Contrast for RGB-D Saliency Detection

Uncertainty Inspired RGB-D Saliency Detection

Siamese Network for RGB-D Salient Object Detection and Beyond

Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection

Cascade Graph Neural Networks for RGB-D Salient Object Detection

Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection

Depth Quality Aware Salient Object Detection

Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection

RGB-D Salient Object Detection: A Survey

Accurate RGB-D Salient Object Detection via Collaborative Learning

A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection

RGB-D Salient Object Detection with Cross-Modality Modulation and Selection

Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection

Multi-level Cross-modal Interaction Network for RGB-D Salient Object Detection

Cross-Modal Weighting Network for RGB-D Salient Object Detection

Bifurcated backbone strategy for RGB-D salient object detection

Is Depth Really Necessary for Salient Object Detection?

Bilateral Attention Network for RGB-D Salient Object Detection

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

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

Density Map Guided Object Detection in Aerial Images

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

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

CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse

Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks