no code implementations • 5 Dec 2023 • Weijie Wang, Guofeng Mei, Bin Ren, Xiaoshui Huang, Fabio Poiesi, Luc van Gool, Nicu Sebe, Bruno Lepri
The cornerstone of ZeroReg is the novel transfer of image features from keypoints to the point cloud, enriched by aggregating information from 3D geometric neighborhoods.
1 code implementation • 4 Dec 2023 • Guofeng Mei, Luigi Riz, Yiming Wang, Fabio Poiesi
Zero-shot 3D point cloud understanding can be achieved via 2D Vision-Language Models (VLMs).
no code implementations • 25 Nov 2023 • Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, Yongshun Gong
This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud, thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object.
1 code implementation • 28 Jul 2023 • Youjie Zhou, Guofeng Mei, Yiming Wang, Fabio Poiesi, Yi Wan
This paper presents an investigation into the estimation of optical and scene flow using RGBD information in scenarios where the RGB modality is affected by noise or captured in dark environments.
no code implementations • 23 May 2023 • Xiaoshui Huang, Guofeng Mei, Jian Zhang
The emerging topic of cross-source point cloud (CSPC) registration has attracted increasing attention with the fast development background of 3D sensor technologies.
no code implementations • CVPR 2023 • Guofeng Mei, Hao Tang, Xiaoshui Huang, Weijie Wang, Juan Liu, Jian Zhang, Luc van Gool, Qiang Wu
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data.
no code implementations • 12 Feb 2023 • Qianliang Wu, Yaqi Shen, Haobo Jiang, Guofeng Mei, Yaqing Ding, Lei Luo, Jin Xie, Jian Yang
Point Cloud Registration is a fundamental and challenging problem in 3D computer vision.
1 code implementation • 17 Oct 2022 • Guofeng Mei, Fabio Poiesi, Cristiano Saltori, Jian Zhang, Elisa Ricci, Nicu Sebe
Probabilistic 3D point cloud registration methods have shown competitive performance in overcoming noise, outliers, and density variations.
1 code implementation • 6 Oct 2022 • Guofeng Mei, Cristiano Saltori, Fabio Poiesi, Jian Zhang, Elisa Ricci, Nicu Sebe, Qiang Wu
Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
no code implementations • 5 Feb 2022 • Guofeng Mei, Litao Yu, Qiang Wu, Jian Zhang, Mohammed Bennamoun
This paper proposes a general unsupervised approach, named \textbf{ConClu}, to perform the learning of point-wise and global features by jointly leveraging point-level clustering and instance-level contrasting.
no code implementations • 29 Dec 2021 • Guofeng Mei, Xiaoshui Huang, Litao Yu, Jian Zhang, Mohammed Bennamoun
Generating a set of high-quality correspondences or matches is one of the most critical steps in point cloud registration.
no code implementations • 23 Nov 2021 • Xiaoshui Huang, Zongyi Xu, Guofeng Mei, Sheng Li, Jian Zhang, Yifan Zuo, Yucheng Wang
To solve this challenge, we propose a new data-driven registration algorithm by investigating deep generative neural networks to point cloud registration.
no code implementations • 3 Mar 2021 • Xiaoshui Huang, Guofeng Mei, Jian Zhang, Rana Abbas
This survey conducts a comprehensive survey, including both same-source and cross-source registration methods, and summarize the connections between optimization-based and deep learning methods, to provide further research insight.
1 code implementation • CVPR 2020 • Xiaoshui Huang, Guofeng Mei, Jian Zhang
We present a fast feature-metric point cloud registration framework, which enforces the optimisation of registration by minimising a feature-metric projection error without correspondences.