EFFICIENT TRAINING OF SEMANTIC POINT CLOUD SEGMENTATION VIA ACTIVE LEARNING
With the development of LiDAR and photogrammetric techniques, more and more point clouds are available with high density and in large areas. Point cloud interpretation is an important step before many real applications like 3D city modelling. Many supervised machine learning techniques have been ada...
Main Authors: | Y. Lin, G. Vosselman, Y. Cao, M. Y. Yang |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2020-08-01
|
Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-2-2020/243/2020/isprs-annals-V-2-2020-243-2020.pdf |
Similar Items
-
LABEL-EFFICIENT DEEP LEARNING-BASED SEMANTIC SEGMENTATION OF BUILDING POINT CLOUDS AT LOD3 LEVEL
by: Y. Cao, et al.
Published: (2021-06-01) -
A PRE-TRAINING METHOD FOR 3D BUILDING POINT CLOUD SEMANTIC SEGMENTATION
by: Y. Cao, et al.
Published: (2022-05-01) -
Point cloud segmentation for urban scene classification
by: G. Vosselman
Published: (2013-10-01) -
Learning semantic segmentation of large-scale point clouds with random sampling
by: Hu, Q, et al.
Published: (2021) -
EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS
by: Y. Xie, et al.
Published: (2021-06-01)