A point cloud as a collection of points is poised to bring about a revolution in acquiring and
generating three‐dimensional (3D) surface information of an object in 3D reconstruction …

Abstract 3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made great …

Abstract Visual Simultaneous Localization and Map** (VSLAM) technology has become a
key technology in autonomous driving and robot navigation. Relying on camera sensors …

Point cloud registration is essential for many applications. However, existing real datasets
require extremely tedious and costly annotations, yet may not provide accurate camera …

Most of the existing RGB-D simultaneous localization and map** (SLAM) systems are
based on point features or point-line features or point-plane features. However, the existing …

Semantic understanding of 3D environments is critical for both the unmanned system and
the human involved virtual/augmented reality (VR/AR) immersive experience. Spatially …

A high-quality 4D geometry and texture reconstruction for human activities usually requires
multiview perceptions via highly structured multi-camera setup, where both the specifically …

We propose INS-Conv, an INcremental Sparse Convolutional network which enables online
accurate 3D semantic and instance segmentation. Benefiting from the incremental nature of …

Although the existing localization and map** (SLAM) technology of indoor mobile robot
has made great development, its intelligence and environmental perception ability still …

Indirect methods for visual SLAM are gaining popularity due to their robustness to
environmental variations. ORB-SLAM2 (Mur-Artal and Tardós, 2017) is a benchmark method …