15 citations · 17 across the 3 of their papers we have counts for
7 papers · 1 filter
3D Hierarchical Refinement and Augmentation for Unsupervised Learning of Depth and Pose from Monocular Video
Guangming Wang, Jiquan Zhong, Shijie Zhao +3
Depth and ego-motion estimations are essential for the localization and navigation of autonomous robots and autonomous driving. Recent studies make it possible to learn the per-pix…
Efficient 3D Deep LiDAR Odometry
Guangming Wang, Xinrui Wu, Shuyang Jiang +2
An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper. In this architecture, the projection-aware representat…
Motion Projection Consistency Based 3D Human Pose Estimation with Virtual Bones from Monocular Videos
Guangming Wang, Honghao Zeng, Ziliang Wang +2
Real-time 3D human pose estimation is crucial for human-computer interaction. It is cheap and practical to estimate 3D human pose only from monocular video. However, recent bone sp…
End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences
Zhijian Qiao, Huanshu Wei, Zhe Liu +2
3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and i…
Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments
Hanjiang Hu, Hesheng Wang, Zhe Liu +3
Visual localization is a crucial problem in mobile robotics and autonomous driving. One solution is to retrieve images with known pose from a database for the localization of query…
SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles
Zhe Liu, Chuanzhe Suo, Shunbo Zhou +4
Place recognition and loop-closure detection are main challenges in the localization, mapping and navigation tasks of self-driving vehicles. In this paper, we solve the loop-closur…