Add to ORKGIn recent years, Deep Learning (DL) techniques and large amounts of pointwise labels are employed to segment point clouds of the built environment. However, annotating pointwise labels is a time-consuming task. To address this issue, we propose a label-efficient DL network that obtains per-point semantic labels of LoD3 (Level-of-Detail) building point clouds with limited supervision. Experimentally, we compared our approach to the fully supervised DL methods, and we find our approach achieved comparable results on the ArCH Data Set, with only 10% of labelled training data obtained from fully supervised methods as input
This master thesis provides in-depth explanations of how deep learning and graph theory can be used ...
In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and...
While modern deep learning algorithms for semantic segmentation of airborne laser scanning (ALS) poi...
In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise la...
In recent years, Deep Learning (DL) techniques and large amounts of pointwise labels are employed to...
In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise la...
In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmenta...
In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmenta...
Abstract. As a result of the success of Deep Learning (DL) techniques, DL-based approaches for extra...
Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great var...
As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever incr...
Manually labelling point cloud scenes for use as training data in machine learning applications is a...
With the development of LiDAR and photogrammetric techniques, more and more point clouds are availab...
In this work, the authors present an artificial intelligence (AI)-based semantic segmentation approa...
During the last decade, the use of semantic models of 3D buildings and structures kept growing, fost...
This master thesis provides in-depth explanations of how deep learning and graph theory can be used ...
In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and...
While modern deep learning algorithms for semantic segmentation of airborne laser scanning (ALS) poi...
In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise la...
In recent years, Deep Learning (DL) techniques and large amounts of pointwise labels are employed to...
In recent research, fully supervised Deep Learning (DL) techniques and large amounts of pointwise la...
In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmenta...
In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmenta...
Abstract. As a result of the success of Deep Learning (DL) techniques, DL-based approaches for extra...
Cultural Heritage is a testimony of past human activity, and, as such, its objects exhibit great var...
As Building Information Modelling (BIM) thrives, geometry becomes no longer sufficient; an ever incr...
Manually labelling point cloud scenes for use as training data in machine learning applications is a...
With the development of LiDAR and photogrammetric techniques, more and more point clouds are availab...
In this work, the authors present an artificial intelligence (AI)-based semantic segmentation approa...
During the last decade, the use of semantic models of 3D buildings and structures kept growing, fost...
This master thesis provides in-depth explanations of how deep learning and graph theory can be used ...
In the geomatics domain the use of deep learning, a subset of machine learning, is becoming more and...
While modern deep learning algorithms for semantic segmentation of airborne laser scanning (ALS) poi...