(ECCV2022)MVSalNet:Multi-View Augmentation for RGB-D Salient Object Detection
Jiayuan Zhou1, Lijun Wang1, Huchuan Lu1,2, Kaining Huang3, Xinchu Shi3 , Bocong Liu3
1 Dalian University of Technology
2 Peng Cheng Laboratory
3 Meituan
The code of MVSalNet in ECCV2022
The depth map and RGB images are from two different modalities with significant cross-modal gap.
The 3D geometry contained in depth map can be used to render the input image under different views.
We present a new framework for RGB-D SOD with multi-view augmentation, which can effectively leverage the geometry information carried in input depth maps.
We design a multi-view saliency prediction network with dynamic filtering modules, which can not only enhance saliency prediction in each single view, but also enables cross-view prediction fusion, yielding more accurate SOD results.
Reconstruct the 3D point cloud based on the input scene depth.
Project the point cloud to a specific target view to render the RGB image.
Single-view saliency prediction module
Multi-view fusion module
A new RGB-D salient object detection (SOD) framework to take full advantages of 3D geometry information contained in depth maps.
A multi-view salient detection network (MVSalNet).
Experiments on six popular benchmarks verify the effectiveness.