[HTML][HTML] Change detection of urban objects using 3D point clouds: A review
Over recent decades, 3D point clouds have been a popular data source applied in automatic
change detection in a wide variety of applications. Compared with 2D images, using 3D …
change detection in a wide variety of applications. Compared with 2D images, using 3D …
Context-aware network for semantic segmentation toward large-scale point clouds in urban environments
C Liu, D Zeng, A Akbar, H Wu, S Jia… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Point cloud semantic segmentation in urban scenes plays a vital role in intelligent city
modeling, autonomous driving, and urban planning. Point cloud semantic segmentation …
modeling, autonomous driving, and urban planning. Point cloud semantic segmentation …
Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification
The classification of airborne laser scanning (ALS) point clouds is a critical task of remote
sensing and photogrammetry fields. Although recent deep learning-based methods have …
sensing and photogrammetry fields. Although recent deep learning-based methods have …
Semantics-aided 3D change detection on construction sites using UAV-based photogrammetric point clouds
As the key to the construction progress monitoring, methods and strategies for change
detection using 3D point clouds from various sources have been investigated for years …
detection using 3D point clouds from various sources have been investigated for years …
A new weakly supervised approach for ALS point cloud semantic segmentation
Although novel point cloud semantic segmentation schemes that continuously surpass state-
of-the-art results exist, the success of learning an effective model typically relies on the …
of-the-art results exist, the success of learning an effective model typically relies on the …
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 …
MCTNet: Multiscale cross-attention based transformer network for semantic segmentation of large-scale point cloud
In this work, we implement a hybrid method to utilize sufficient information by aggregating
both fine-grained and globally contextual features for point cloud semantic segmentation …
both fine-grained and globally contextual features for point cloud semantic segmentation …
Road-side individual tree segmentation from urban MLS point clouds using metric learning
P Wang, Y Tang, Z Liao, Y Yan, L Dai, S Liu, T Jiang - Remote Sensing, 2023 - mdpi.com
As one of the most important components of urban space, an outdated inventory of road-side
trees may misguide managers in the assessment and upgrade of urban environments …
trees may misguide managers in the assessment and upgrade of urban environments …
RailSeg: Learning Local-Global Feature Aggregation with Contextual Information for Railway Point Cloud Semantic Segmentation
Incomplete or outdated inventories of railway infrastructures may disrupt the railway sector's
administration and maintenance of transportation infrastructure, thus posing potential threats …
administration and maintenance of transportation infrastructure, thus posing potential threats …
Local and global structure for urban ALS point cloud semantic segmentation with ground-aware attention
Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in
geoinformation production. As a critical step for interpretation, accurate semantic …
geoinformation production. As a critical step for interpretation, accurate semantic …