no code implementations • 23 May 2024 • Chaokang Jiang, Dalong Du, Jiuming Liu, Siting Zhu, Zhenqiang Liu, Zhuang Ma, Zhujin Liang, Jie zhou
Point Cloud Interpolation confronts challenges from point sparsity, complex spatiotemporal dynamics, and the difficulty of deriving complete 3D point clouds from sparse temporal information.
no code implementations • 23 May 2024 • Jiuming Liu, Jinru Han, Lihao Liu, Angelica I. Aviles-Rivero, Chaokang Jiang, Zhe Liu, Hesheng Wang
Point cloud videos effectively capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing 3D world we live in.
1 code implementation • 28 Feb 2024 • Chaokang Jiang, Guangming Wang, Jiuming Liu, Hesheng Wang, Zhuang Ma, Zhenqiang Liu, Zhujin Liang, Yi Shan, Dalong Du
We present a novel approach from the perspective of auto-labelling, aiming to generate a large number of 3D scene flow pseudo labels for real-world LiDAR point clouds.
1 code implementation • 29 Nov 2023 • Jiuming Liu, Guangming Wang, Weicai Ye, Chaokang Jiang, Jinru Han, Zhe Liu, Guofeng Zhang, Dalong Du, Hesheng Wang
Furthermore, we also develop an uncertainty estimation module within diffusion to evaluate the reliability of estimated scene flow.
1 code implementation • ICCV 2023 • Jiuming Liu, Guangming Wang, Zhe Liu, Chaokang Jiang, Marc Pollefeys, Hesheng Wang
Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally.
no code implementations • 27 Sep 2022 • Chaokang Jiang, Guangming Wang, Yanzi Miao, Hesheng Wang
The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds.
no code implementations • 15 Sep 2022 • Chaokang Jiang, Guangming Wang, Jinxing Wu, Yanzi Miao, Hesheng Wang
Promising complementarity exists between the texture features of color images and the geometric information of LiDAR point clouds.
no code implementations • 11 Sep 2022 • Guangming Wang, Zhiheng Feng, Chaokang Jiang, Hesheng Wang
Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network.
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.