Lidar-based place recognition for autonomous driving: A survey

Y Zhang, P Shi, J Li - ACM Computing Surveys, 2024‏ - dl.acm.org
LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich three-dimensional information, and stability in harsh …

A review of non-rigid transformations and learning-based 3D point cloud registration methods

S Monji-Azad, J Hesser, N Löw - ISPRS journal of photogrammetry and …, 2023‏ - Elsevier
Point cloud registration is a research field where the spatial relationship between two or
more sets of points in space is determined. Point clouds are found in multiple applications …

3D registration with maximal cliques

X Zhang, J Yang, S Zhang… - Proceedings of the IEEE …, 2023‏ - openaccess.thecvf.com
As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to
seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration …

Geometric transformer for fast and robust point cloud registration

Z Qin, H Yu, C Wang, Y Guo… - Proceedings of the …, 2022‏ - openaccess.thecvf.com
We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …

Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds

Y Zhang, Q Hu, G Xu, Y Ma, J Wan… - Proceedings of the …, 2022‏ - openaccess.thecvf.com
We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the
memory and computational cost, existing point-based pipelines usually adopt task-agnostic …

Rotation-invariant transformer for point cloud matching

H Yu, Z Qin, J Hou, M Saleh, D Li… - Proceedings of the …, 2023‏ - openaccess.thecvf.com
The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted
descriptors. However, it is widely despised by recent deep matchers that obtain the rotation …

Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration

S Ao, Q Hu, H Wang, K Xu… - Proceedings of the IEEE …, 2023‏ - openaccess.thecvf.com
An ideal point cloud registration framework should have superior accuracy, acceptable
efficiency, and strong generalizability. However, this is highly challenging since existing …

Geotransformer: Fast and robust point cloud registration with geometric transformer

Z Qin, H Yu, C Wang, Y Guo, Y Peng… - … on Pattern Analysis …, 2023‏ - ieeexplore.ieee.org
We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods have shown great potential through bypassing the detection …

Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration

H Yu, F Li, M Saleh, B Busam… - Advances in Neural …, 2021‏ - proceedings.neurips.cc
We study the problem of extracting correspondences between a pair of point clouds for
registration. For correspondence retrieval, existing works benefit from matching sparse …

Sc2-pcr: A second order spatial compatibility for efficient and robust point cloud registration

Z Chen, K Sun, F Yang, W Tao - Proceedings of the IEEE …, 2022‏ - openaccess.thecvf.com
In this paper, we present a second order spatial compatibility (SC^ 2) measure based
method for efficient and robust point cloud registration (PCR), called SC^ 2-PCR. Firstly, we …