Search Results for author: Xinrui Wu

Found 6 papers, 2 papers with code

Efficient 3D Deep LiDAR Odometry

1 code implementation3 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.

Pose Estimation

PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization

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.

Pose Estimation

Hierarchical Attention Learning of Scene Flow in 3D Point Clouds

no code implementations12 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.

Autonomous Driving Optical Flow Estimation +1

Residual 3D Scene Flow Learning with Context-Aware Feature Extraction

no code implementations10 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.

Autonomous Driving Scene Flow Estimation +1

Pseudo-LiDAR for Visual Odometry

no code implementations4 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.

Stereo Matching Visual Odometry

DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds

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.

Scene Flow Estimation

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