no code implementations • 27 Mar 2024 • Jiuming Liu, Dong Zhuo, Zhiheng Feng, Siting Zhu, Chensheng Peng, Zhe Liu, Hesheng Wang
Image pixels are pre-organized as pseudo points for image-to-point structure alignment.
no code implementations • 12 Mar 2024 • Siting Zhu, Renjie Qin, Guangming Wang, Jiuming Liu, Hesheng Wang
We propose SemGauss-SLAM, the first semantic SLAM system utilizing 3D Gaussian representation, that enables accurate 3D semantic mapping, robust camera tracking, and high-quality rendering in real-time.
1 code implementation • 11 Mar 2024 • Jiuming Liu, Ruiji Yu, Yian Wang, Yu Zheng, Tianchen Deng, Weicai Ye, Hesheng Wang
In this paper, we propose a novel SSM-based point cloud processing backbone, named Point Mamba, with a causality-aware ordering mechanism.
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.
no code implementations • 29 Nov 2023 • Yu Zheng, Guangming Wang, Jiuming Liu, Marc Pollefeys, Hesheng Wang
Through the hash-based representation, we propose the Spherical Frustum sparse Convolution (SFC) and Frustum Fast Point Sampling (F2PS) to convolve and sample the points stored in spherical frustums respectively.
no code implementations • 10 Sep 2023 • Liang Song, Guangming Wang, Jiuming Liu, Zhenyang Fu, Yanzi Miao, Hesheng
By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results.
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.