Deep learning for 3d point clouds: A survey
Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
Deep learning-based 3D point cloud classification: A systematic survey and outlook
In recent years, point cloud representation has become one of the research hotspots in the
field of computer vision, and has been widely used in many fields, such as autonomous …
field of computer vision, and has been widely used in many fields, such as autonomous …
SCF-Net: Learning spatial contextual features for large-scale point cloud segmentation
How to learn effective features from large-scale point clouds for semantic segmentation has
attracted increasing attention in recent years. Addressing this problem, we propose a …
attracted increasing attention in recent years. Addressing this problem, we propose a …
Randla-net: Efficient semantic segmentation of large-scale point clouds
We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …
relying on expensive sampling techniques or computationally heavy pre/post-processing …
Learning semantic segmentation of large-scale point clouds with random sampling
We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By
relying on expensive sampling techniques or computationally heavy pre/post-processing …
relying on expensive sampling techniques or computationally heavy pre/post-processing …
LFT-Net: Local feature transformer network for point clouds analysis
6G network enables the rapid connection of autonomous vehicles, the generated internet of
vehicles establishes a large-scale point cloud, which requires automatic point cloud analysis …
vehicles establishes a large-scale point cloud, which requires automatic point cloud analysis …
Are we hungry for 3D LiDAR data for semantic segmentation? A survey of datasets and methods
B Gao, Y Pan, C Li, S Geng… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
3D semantic segmentation is a fundamental task for robotic and autonomous driving
applications. Recent works have been focused on using deep learning techniques, whereas …
applications. Recent works have been focused on using deep learning techniques, whereas …
Spatial information guided convolution for real-time RGBD semantic segmentation
3D spatial information is known to be beneficial to the semantic segmentation task. Most
existing methods take 3D spatial data as an additional input, leading to a two-stream …
existing methods take 3D spatial data as an additional input, leading to a two-stream …
[HTML][HTML] Scanning technologies to building information modelling: A review
Building information modelling (BIM) is evolving significantly in the architecture, engineering
and construction industries. BIM involves various remote-sensing tools, procedures and …
and construction industries. BIM involves various remote-sensing tools, procedures and …
Pyramid architecture for multi-scale processing in point cloud segmentation
Semantic segmentation of point cloud data is a critical task for autonomous driving and other
applications. Recent advances of point cloud segmentation are mainly driven by new …
applications. Recent advances of point cloud segmentation are mainly driven by new …