A tutorial review on point cloud registrations: principle, classification, comparison, and technology challenges
A point cloud as a collection of points is poised to bring about a revolution in acquiring and
generating three‐dimensional (3D) surface information of an object in 3D reconstruction …
generating three‐dimensional (3D) surface information of an object in 3D reconstruction …
Sampling-attention deep learning network with transfer learning for large-scale urban point cloud semantic segmentation
Targeting the development of smart cities to facilitate the significant analysis of large-scale
urban for construction and update. This research develops a new method named transfer …
urban for construction and update. This research develops a new method named transfer …
3d-siamrpn: An end-to-end learning method for real-time 3d single object tracking using raw point cloud
3D single object tracking is a key issue for autonomous following robot, where the robot
should robustly track and accurately localize the target for efficient following. In this paper …
should robustly track and accurately localize the target for efficient following. In this paper …
[HTML][HTML] Classification of large-scale mobile laser scanning data in urban area with LightGBM
Automatic point cloud classification (PCC) is a challenging task in large-scale urban point
clouds due to the heterogeneous density of points, the high number of points and the …
clouds due to the heterogeneous density of points, the high number of points and the …
Fast sequence-matching enhanced viewpoint-invariant 3-d place recognition
Recognizing the same place undervariant viewpoint differences is the fundamental
capability for human beings and animals. However, such a strong place recognition ability in …
capability for human beings and animals. However, such a strong place recognition ability in …
A heterogeneous 3D map-based place recognition solution using virtual LiDAR and a polar grid height coding image descriptor
Place recognition is widely used for global localization technology. However, the existing
place recognition solutions are limited by the requirement for the same type of sensors to be …
place recognition solutions are limited by the requirement for the same type of sensors to be …
Performance evaluation of 3d keypoint detectors and descriptors on coloured point clouds in subsea environments
The recent development of high-precision subsea optical scanners allows for 3D keypoint
detectors and feature descriptors to be leveraged on point cloud scans from subsea …
detectors and feature descriptors to be leveraged on point cloud scans from subsea …
[HTML][HTML] GSV-NET: A Multi-modal deep learning network for 3D point cloud classification
L Hoang, SH Lee, EJ Lee, KR Kwon - Applied Sciences, 2022 - mdpi.com
Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser
to estimate the distance between the LiDAR sensor and objects, is an effective remote …
to estimate the distance between the LiDAR sensor and objects, is an effective remote …
Matching of crushed highly decomposed granite particles using 3D SHOT descriptors
Z Zhu, J Wang - Géotechnique, 2023 - icevirtuallibrary.com
Owing to the complex morphology and the fragility of highly decomposed granite (HDG), it is
still a challenge to track the breakage process of HDG particles within a deformed sample in …
still a challenge to track the breakage process of HDG particles within a deformed sample in …
FCPNet: A method for rescuing feature information loss in scaling change for urban 3D Point cloud classification
Y Jiang, G Zhou - IEEE Journal of Selected Topics in Applied …, 2024 - ieeexplore.ieee.org
The loss of feature information during scale propagation in the deep learning method
usually causes a big misclassification rate for many complex urban scenes. For this reason …
usually causes a big misclassification rate for many complex urban scenes. For this reason …