A comprehensive survey on point cloud registration
Registration is a transformation estimation problem between two point clouds, which has a
unique and critical role in numerous computer vision applications. The developments of …
unique and critical role in numerous computer vision applications. The developments of …
Lidar-based place recognition for autonomous driving: A survey
LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich three-dimensional information, and stability in harsh …
measurement distance, rich three-dimensional information, and stability in harsh …
Geometric transformer for fast and robust point cloud registration
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 …
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …
Regtr: End-to-end point cloud correspondences with transformers
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 …
focus on learning feature descriptors and continue to rely on nearest-neighbor feature …
3D registration with maximal cliques
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 …
seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration …
Predator: Registration of 3d point clouds with low overlap
We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention
to the overlap region. Different from previous work, our model is specifically designed to …
to the overlap region. Different from previous work, our model is specifically designed to …
Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Arguably one of the top success stories of deep learning is transfer learning. The finding that
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …
pre-training a network on a rich source set (eg, ImageNet) can help boost performance once …
Cofinet: Reliable coarse-to-fine correspondences for robust pointcloud registration
We study the problem of extracting correspondences between a pair of point clouds for
registration. For correspondence retrieval, existing works benefit from matching sparse …
registration. For correspondence retrieval, existing works benefit from matching sparse …
Rotation-invariant transformer for point cloud matching
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 …
descriptors. However, it is widely despised by recent deep matchers that obtain the rotation …
Pointdsc: Robust point cloud registration using deep spatial consistency
Removing outlier correspondences is one of the critical steps for successful feature-based
point cloud registration. Despite the increasing popularity of introducing deep learning …
point cloud registration. Despite the increasing popularity of introducing deep learning …