Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey
The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …
deep learning (DL). However, the latter faces various issues, including the lack of data or …
Geometric clifford algebra networks
Abstract We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …
systems. GCANs are based on symmetry group transformations using geometric (Clifford) …
Svqnet: Sparse voxel-adjacent query network for 4d spatio-temporal lidar semantic segmentation
LiDAR-based semantic perception tasks are critical yet challenging for autonomous driving.
Due to the motion of objects and static/dynamic occlusion, temporal information plays an …
Due to the motion of objects and static/dynamic occlusion, temporal information plays an …
SVASeg: Sparse voxel-based attention for 3D LiDAR point cloud semantic segmentation
L Zhao, S Xu, L Liu, D Ming, W Tao - Remote Sensing, 2022 - mdpi.com
3D LiDAR has become an indispensable sensor in autonomous driving vehicles. In LiDAR-
based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot …
based 3D point cloud semantic segmentation, most voxel-based 3D segmentors cannot …
Spatial-temporal transformer for 3d point cloud sequences
Effective learning of spatial-temporal information within a point cloud sequence is highly
important for many down-stream tasks such as 4D semantic segmentation and 3D action …
important for many down-stream tasks such as 4D semantic segmentation and 3D action …
TASeg: Temporal Aggregation Network for LiDAR Semantic Segmentation
Training deep models for LiDAR semantic segmentation is challenging due to the inherent
sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity …
sparsity of point clouds. Utilizing temporal data is a natural remedy against the sparsity …
Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms
R Guo, J Xie, J Zhu, R Cheng, Y Zhang, X Zhang… - … and Electronics in …, 2023 - Elsevier
Plant phenotyping is essential for understanding and managing plant growth and
development. 3D point clouds provide a better understanding of plant 3D structures. Point …
development. 3D point clouds provide a better understanding of plant 3D structures. Point …
Anchor-based spatio-temporal attention 3-d convolutional networks for dynamic 3-d point cloud sequences
With the rapid development of measurement technology, light detection and ranging
(LiDAR) and depth cameras are widely used in the perception of the 3-D environment …
(LiDAR) and depth cameras are widely used in the perception of the 3-D environment …
p^ 3-net: Part mobility parsing from point cloud sequences via learning explicit point correspondence
Understanding an articulated 3D object with its movable parts is an essential skill for an
intelligent agent. This paper presents a novel approach to parse 3D part mobility from point …
intelligent agent. This paper presents a novel approach to parse 3D part mobility from point …
PTFD-Net: A Sliding Detection Algorithm Combining Point Cloud Sequences and Tactile Sequences Information
T Li, Y Yan, J An, G Chen, Y Wang - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Sliding detection can effectively enhance the stability of robot grasping operations. Methods
relying solely on 2-D vision or tactile information for sliding detection often exhibit limited …
relying solely on 2-D vision or tactile information for sliding detection often exhibit limited …