Semantic segmentation of mobile mapping point clouds via multi-view label transfer

Peters, Torben; Brenner, Claus; Schindler, Konrad

doi:10.1016/j.isprsjprs.2023.05.018

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

3D Can Be Explored In 2D : Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

Caunes,

Chateau,

Frémont

2024

2024 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

3D Can Be Explored In 2D : Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

Caunes,

Chateau,

Frémont

2024

2024 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Multimodal Co-Learning for Building Change Detection: A Domain Adaptation Framework Using VHR Images and Digital Surface Models

Xie,

Yuan,

Zhu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

In this article, we propose a multimodal co-learning framework for building change detection. This framework can be adopted to jointly train a Siamese bitemporal image network and a height difference map (HDiff) network with labeled source data and unlabeled target data pairs. Three co-learning combinations (vanilla co-learning, fusion co-learning, and detached fusion colearning) are proposed and investigated with two types of colearning loss functions within our framework. Our experimental results demonstrate that the proposed methods are able to take advantage of unlabeled target data pairs and therefore enhance the performance of single-modal neural networks on the target data. In addition, our synthetic-to-real experiments demonstrate that the recently published synthetic dataset SMARS is feasible to be used in real change detection scenarios, where the optimal result is with the F1 score of 79.29%.

show abstract

Semantic segmentation of mobile mapping point clouds via multi-view label transfer

Cited by 2 publications

References 40 publications

3D Can Be Explored In 2D : Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

3D Can Be Explored In 2D : Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

Multimodal Co-Learning for Building Change Detection: A Domain Adaptation Framework Using VHR Images and Digital Surface Models

Contact Info

Product

Resources

About