Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method-Reference-Cited by-同舟云学术

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Point cloud semantic segmentation of grotto scenes using the knowledge-guided deep learning method

Published:2024-08-04 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Yang Su¹,Hou Miaole²,Li Songnian³

Affiliation:

1. College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing, People’s Republic of China

2. School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, People’s Republic of China

3. Department of Civil Engineering, Toronto Metropolitan University, Toronto, Canada

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2385081

Reference107 articles.

1. Ahn J. and K. y. Wohn. 2015. “Lessons Learned from Reconstruction of a Virtual Grotto - From Point Cloud to Immersive Virtual Environment.” Paper Presented at the 2015 Digital Heritage 28 Sept.–2 Oct. 2015.

2. 3D MODELING AND DATA ENRICHMENT IN DIGITAL RECONSTRUCTION OF ARCHITECTURAL HERITAGE

3. Armeni Iro Ozan Sener Amir R Zamir Helen Jiang Ioannis Brilakis Martin Fischer and Silvio Savarese. 2016. “3D Semantic Parsing of Large-Scale Indoor Spaces.” Paper Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

4. A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

5. Behley Jens Martin Garbade Andres Milioto Jan Quenzel Sven Behnke Cyrill Stachniss and Jurgen Gall. 2019. “Semantickitti: A Dataset for Semantic Scene Understanding of Lidar Sequences.” Paper Presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision.

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