An Improved Explainable Point Cloud Classifier (XPCC)-Reference-Cited by-同舟云学术

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An Improved Explainable Point Cloud Classifier (XPCC)

Published:2023-02 Issue:1 Volume:4 Page:71-80
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Arnold Nicholas I.¹^ORCID,Angelov Plamen¹^ORCID,Atkinson Peter M.¹^ORCID

Affiliation:

1. School of Computing and Communications Lancaster University, Lancaster University, Lancaster, U.K.

Funder

Cumbria Innovations Platform

Lancaster University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Artificial Intelligence

Link

http://xplorestaging.ieee.org/ielx7/9078688/10023979/09714048.pdf?arnumber=9714048

Reference47 articles.

1. SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

2. ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes

3. A method for registration of 3-D shapes

4. Empirical Approach to Machine Learning

5. 3D shapeNets: A deep representation for volumetric shapes;wu;Proc IEEE Conf Comput Vis Pattern Recognit,0

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning for 3D object recognition: A survey;Neurocomputing;2024-12

2. Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation;International Journal of Digital Earth;2024-09-02

3. AI Security for Geoscience and Remote Sensing: Challenges and future trends;IEEE Geoscience and Remote Sensing Magazine;2023-06

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