[HTML][HTML] MVPNet: A multi-scale voxel-point adaptive fusion network for point cloud semantic segmentation in urban scenes
Point cloud semantic segmentation, which contributes to scene understanding at different
scales, is crucial for three-dimensional reconstruction and digital twin cities. However …
scales, is crucial for three-dimensional reconstruction and digital twin cities. However …
Recurrent residual dual attention network for airborne laser scanning point cloud semantic segmentation
Kernel point convolution (KPConv) can effectively represent the point features of point cloud
data. However, KPConv-based methods just consider the local information of each point …
data. However, KPConv-based methods just consider the local information of each point …
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 …
Human vision based 3d point cloud semantic segmentation of large-scale outdoor scenes
This paper proposes EyeNet, a novel semantic segmentation network for point clouds that
addresses the critical yet often overlooked parameter of coverage area size. Inspired by …
addresses the critical yet often overlooked parameter of coverage area size. Inspired by …
Multi-level context feature fusion for semantic segmentation of ALS point cloud
Semantic segmentation of airborne laser scanning (ALS) point clouds using deep learning is
a hot research in remote sensing and photogrammetry. A current trend is to aggregate …
a hot research in remote sensing and photogrammetry. A current trend is to aggregate …
SFL-Net: Slight filter learning network for point cloud semantic segmentation
X Li, Z Zhang, Y Li, M Huang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In recent years, point clouds have been widely used in power-line inspection, smart cities,
autonomous driving, and other fields. Deep learning-based point cloud processing methods …
autonomous driving, and other fields. Deep learning-based point cloud processing methods …
PointMM: Point Cloud Semantic Segmentation CNN under Multi-Spatial Feature Encoding and Multi-Head Attention Pooling
R Chen, J Wu, Y Luo, G Xu - Remote Sensing, 2024 - mdpi.com
For the actual collected point cloud data, there are widespread challenges such as semantic
inconsistency, density variations, and sparse spatial distribution. A network called PointMM …
inconsistency, density variations, and sparse spatial distribution. A network called PointMM …
AGFA-Net: Adaptive global feature augmentation network for point cloud completion
X Liu, Y Ma, K Xu, J Wan, Y Guo - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Completing shapes of point clouds from partial scans is a fundamental problem for 3-D
vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors …
vision and remote sensing. However, recent methods mainly relied on K-nearest neighbors …
Boundary–Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
L He, J She, Q Zhao, X Wen, Y Guan - Applied Sciences, 2023 - mdpi.com
In a point cloud semantic segmentation task, misclassification usually appears on the
semantic boundary. A few studies have taken the boundary into consideration, but they …
semantic boundary. A few studies have taken the boundary into consideration, but they …
[HTML][HTML] DAAL-WS: A weakly-supervised method integrated with data augmentation and active learning strategies for MLS point cloud semantic segmentation
Mobile laser scanning (MLS) point clouds have increasingly been a significant data source
for acquiring accurate three-dimensional (3D) semantic information from complex scenes …
for acquiring accurate three-dimensional (3D) semantic information from complex scenes …