Learning Semantic Segmentation of Large-Scale Point Clouds With Random Sampling

We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing steps, most existing…

Parallax Attention for Unsupervised Stereo Correspondence Learning

Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly use cost volume tec…

Deep Learning for 3D Point Clouds: A Survey

Point cloud learning has lately attracted increasing attention due to its wide applications in many areas, such as computer vision, autonomous driving, and robotics. As a dominating technique in AI…

Stereo Matching Using Multi-Level Cost Volume and Multi-Scale Feature Constancy

Guest Editors Introduction to he Special Issue on RGB-D Vision : Method and A…

3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey