41 citations · 71 across the 9 of their papers we have counts for
15 papers
LiDAR Odometry Methodologies for Autonomous Driving: A Survey
Nikhil Jonnavithula, Yecheng Lyu, Ziming Zhang
Vehicle odometry is an essential component of an automated driving system as it computes the vehicle's position and orientation. The odometry module has a higher demand and impact…
Revisiting 2D Convolutional Neural Networks for Graph-based Applications
Yecheng Lyu, Xinming Huang, Ziming Zhang
Graph convolutional networks (GCNs) are widely used in graph-based applications such as graph classification and segmentation. However, current GCNs have limitations on implementat…
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation
Yecheng Lyu, Xinming Huang, Ziming Zhang
Point cloud patterns are hard to learn because of the implicit local geometry features among the orderless points. In recent years, point cloud representation in 2D space has attra…
LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
Ce Zheng, Yecheng Lyu, Ming Li +1
Deep learning based LiDAR odometry (LO) estimation attracts increasing research interests in the field of autonomous driving and robotics. Existing works feed consecutive LiDAR fra…
TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
Yecheng Lyu, Ming Li, Xinming Huang +3
General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized grap…
Automatic Building and Labeling of HD Maps with Deep Learning
Mahdi Elhousni, Yecheng Lyu, Ziming Zhang +1
In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and…