53 citations · 202 across the 19 of their papers we have counts for
14 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…
Unsupervised Learning of Monocular Depth and Ego-Motion Using Multiple Masks
Guangming Wang, Hesheng Wang, Yiling Liu +1
A new unsupervised learning method of depth and ego-motion using multiple masks from monocular video is proposed in this paper. The depth estimation network and the ego-motion esti…
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences
Guangming Wang, Muyao Chen, Hanwen Liu +3
With the rapid development of measurement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perce…
A Registration-aided Domain Adaptation Network for 3D Point Cloud Based Place Recognition
Zhijian Qiao, Hanjiang Hu, Weiang Shi +3
In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness…
Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds
Guangming Wang, Yehui Yang, Huixin Zhang +2
The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution k…
DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization
Hanjiang Hu, Zhijian Qiao, Ming Cheng +2
Long-Term visual localization under changing environments is a challenging problem in autonomous driving and mobile robotics due to season, illumination variance, etc. Image retrie…
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…
Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization
Hanjiang Hu, Hesheng Wang, Zhe Liu +1
Visual localization is a crucial component in the application of mobile robot and autonomous driving. Image retrieval is an efficient and effective technique in image-based localiz…
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…
Learning Actions from Human Demonstration Video for Robotic Manipulation
Shuo Yang, Wei Zhang, Weizhi Lu +2
Learning actions from human demonstration is an emerging trend for designing intelligent robotic systems, which can be referred as video to command. The performance of such approac…
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…
LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis
Zhe Liu, Shunbo Zhou, Chuanzhe Suo +4
Point cloud based place recognition is still an open issue due to the difficulty in extracting local features from the raw 3D point cloud and generating the global descriptor, and…