no code implementations • 4 Feb 2025 • Guiyu Zhao, Sheng Ao, Ye Zhang, Kai Xu Yulan Guo
Existing correspondence refinement methods mostly follow the paradigm of outlier removal, which either fails to correctly identify the accurate correspondences under extreme outlier ratios, or select too few correct correspondences to support robust registration.
no code implementations • 5 Jan 2025 • Minglin Chen, Longguang Wang, Sheng Ao, Ye Zhang, Kai Xu, Yulan Guo
To fully leverage 2D diffusion priors in geometry and appearance generation, we introduce a semantic-guided geometry diffusion model and a semantic-geometry guided diffusion model which are finetuned on a scene dataset.
1 code implementation • CVPR 2023 • Sheng Ao, Qingyong Hu, Hanyun Wang, Kai Xu, Yulan Guo
Extensive experiments on real-world scenarios demonstrate that our method achieves the best of both worlds in accuracy, efficiency, and generalization.
1 code implementation • CVPR 2021 • Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo
Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction.
Ranked #2 on
Point Cloud Registration
on ETH (trained on 3DMatch)