Deep learning on rail profiles matching

1 code implementation • 18 May 2022 • Kunqi Wang, Daolin Si, Pu Wang, Jing Ge, Peiyuan Ni, Shuguo Wang

Matching the rail cross-section profiles measured on site with the designed profile is a must to evaluate the wear of the rail, which is very important for track maintenance and rail safety.

PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds

1 code implementation • 21 Mar 2020 • Peiyuan Ni, Wenguang Zhang, Xiaoxiao Zhu, Qixin Cao

It takes the whole sparse point clouds as the input and requires no sampling or search process.

Robotics