1 code implementation • 3 Nov 2021 • Guangming Wang, Xinrui Wu, Shuyang Jiang, Zhe Liu, Hesheng Wang
An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper.
1 code implementation • CVPR 2021 • Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang
A novel 3D point cloud learning model for deep LiDAR odometry, named PWCLO-Net, using hierarchical embedding mask optimization is proposed in this paper.
no code implementations • 12 Oct 2020 • Guangming Wang, Xinrui Wu, Zhe Liu, Hesheng Wang
In this paper, a novel hierarchical neural network with double attention is proposed for learning the correlation of point features in adjacent frames and refining scene flow from coarse to fine layer by layer.
no code implementations • 10 Sep 2021 • Guangming Wang, Yunzhe Hu, Xinrui Wu, Hesheng Wang
To solve the first problem, a novel context-aware set convolution layer is proposed in this paper to exploit contextual structure information of Euclidean space and learn soft aggregation weights for local point features.
no code implementations • 4 Sep 2022 • Huiying Deng, Guangming Wang, Zhiheng Feng, Chaokang Jiang, Xinrui Wu, Yanzi Miao, Hesheng Wang
In order to make full use of the rich point cloud information provided by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted.
no code implementations • ICCV 2023 • Chensheng Peng, Guangming Wang, Xian Wan Lo, Xinrui Wu, Chenfeng Xu, Masayoshi Tomizuka, Wei Zhan, Hesheng Wang
Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation.