1 code implementation • CVPR 2022 • Zi Jian Yew, Gim Hee Lee
Despite recent success in incorporating learning into point cloud registration, many works focus on learning feature descriptors and continue to rely on nearest-neighbor feature matching and outlier filtering through RANSAC to obtain the final set of correspondences for pose estimation.
1 code implementation • 1 Nov 2021 • Zi Jian Yew, Gim Hee Lee
Transformation Synchronization is the problem of recovering absolute transformations from a given set of pairwise relative motions.
no code implementations • 26 Mar 2021 • Zi Jian Yew, Gim Hee Lee
We propose a method for detecting structural changes in a city using images captured from vehicular mounted cameras over traversals at two different times.
5 code implementations • CVPR 2020 • Zi Jian Yew, Gim Hee Lee
The hard assignments of closest point correspondences based on spatial distances are sensitive to the initial rigid transformation and noisy/outlier points, which often cause ICP to converge to wrong local minima.
1 code implementation • CVPR 2019 • Ziquan Lan, Zi Jian Yew, Gim Hee Lee
Furthermore, we show that by using a Gaussian-Uniform mixture model, our approach degenerates to the formulation of a state-of-the-art approach for robust indoor reconstruction.
1 code implementation • ECCV 2018 • Zi Jian Yew, Gim Hee Lee
In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision.
Ranked #6 on Point Cloud Registration on KITTI