1 citations · 1 across the 7 of their papers we have counts for
7 papers
MambaFlow: A Novel and Flow-guided State Space Model for Scene Flow Estimation
Jiehao Luo, Jintao Cheng, Xiaoyu Tang +3
Scene flow estimation aims to predict 3D motion from consecutive point cloud frames, which is of great interest in autonomous driving field. Existing methods face challenges such a…
SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving
Qingwen Zhang, Yi Yang, Peizheng Li +2
Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. This detailed, point-level, information can help autonomous vehicles to accurately predict and…
DUFOMap: Efficient Dynamic Awareness Mapping
Daniel Duberg, Qingwen Zhang, MingKai Jia +1
The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical bench…
DeFlow: Decoder of Scene Flow Network in Autonomous Driving
Qingwen Zhang, Yi Yang, Heng Fang +2
Scene flow estimation determines a scene's 3D motion field, by predicting the motion of points in the scene, especially for aiding tasks in autonomous driving. Many networks with l…
RMP: A Random Mask Pretrain Framework for Motion Prediction
Yi Yang, Qingwen Zhang, Thomas Gilles +2
As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we pro…
A Dynamic Points Removal Benchmark in Point Cloud Maps
Qingwen Zhang, Daniel Duberg, Ruoyu Geng +3
In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points co…