4 citations · 7 across the 4 of their papers we have counts for
6 papers
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking
Guangming Wang, Chensheng Peng, Jinpeng Zhang +1
Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected b…
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation
Yueling Shen, Guangming Wang, Hesheng Wang
3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These meth…
NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry
Guangming Wang, Shuaiqi Ren, Hesheng Wang
Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geome…
PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
Guangming Wang, Xinrui Wu, Zhe Liu +1
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper. In this model, the Pyramid…
Hierarchical Attention Learning of Scene Flow in 3D Point Clouds
Guangming Wang, Xinrui Wu, Zhe Liu +1
Scene flow represents the 3D motion of every point in the dynamic environments. Like the optical flow that represents the motion of pixels in 2D images, 3D motion representation of…
Unsupervised Learning of Depth, Optical Flow and Pose with Occlusion from 3D Geometry
Guangming Wang, Chi Zhang, Hesheng Wang +3
In autonomous driving, monocular sequences contain lots of information. Monocular depth estimation, camera ego-motion estimation and optical flow estimation in consecutive frames a…