A Review of Deep Learning-Based Semantic Segmentation for Point Cloud
Article Properties
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DOI (url)
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Publication Date2019/01/01
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Journal
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Indian UGC (journal)
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Refrences94
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Citations7
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Jiaying Zhang ORCID (unauthenticated)
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Xiaoli Zhao ORCID (unauthenticated)
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Zheng Chen ORCID (unauthenticated)
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Zhejun Lu ORCID (unauthenticated)
Cite
Zhang, Jiaying, et al. “A Review of Deep Learning-Based Semantic Segmentation for Point Cloud”. IEEE Access, vol. 7, 2019, pp. 179118-33, https://doi.org/10.1109/access.2019.2958671.
Zhang, J., Zhao, X., Chen, Z., & Lu, Z. (2019). A Review of Deep Learning-Based Semantic Segmentation for Point Cloud. IEEE Access, 7, 179118-179133. https://doi.org/10.1109/access.2019.2958671
Zhang, Jiaying, Xiaoli Zhao, Zheng Chen, and Zhejun Lu. “A Review of Deep Learning-Based Semantic Segmentation for Point Cloud”. IEEE Access 7 (2019): 179118-33. https://doi.org/10.1109/access.2019.2958671.
1.
Zhang J, Zhao X, Chen Z, Lu Z. A Review of Deep Learning-Based Semantic Segmentation for Point Cloud. IEEE Access. 2019;7:179118-33.
Journal Categories
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Technology
Electrical engineering
Electronics
Nuclear engineering
Telecommunication
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Semantic geometric modelling of unstructured indoor point cloud | ISPRS International Journal of Geo-Information |
| 2019 | |
| Two-stream multi-rate recurrent neural network for video-based pedestrian re-identification | IEEE Transactions on Industrial Informatics |
| 2017 | |
| Unstructured point cloud semantic labeling using deep segmentation networks | 2017 | |||
| Spatially-sparse convolutional neural networks | Computer Science |
| 2014 | |
| Nonassociative Markov networks for 3d point cloud classification | 2010 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data | Earth and Space Science |
| 2023 | |
A survey on weakly supervised 3D point cloud semantic segmentation | IET Computer Vision |
| 2023 | |
Power Operation Violation Identification Method Based on Point Cloud Data Preprocessing and Deep Learning under the Architecture of IoT | Journal of Electrical and Computer Engineering |
| 2023 | |
An ENet Semantic Segmentation Method Combined with Attention Mechanism | Computational Intelligence and Neuroscience |
| 2023 | |
Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review | Advances in Civil Engineering |
| 11 | 2022 |
Citations Analysis
The category
Science: Biology (General) 2
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review and was published in 2022. The most recent citation comes from a 2023 study titled A survey on weakly supervised 3D point cloud semantic segmentation. This article reached its peak citation in 2023, with 4 citations. It has been cited in 7 different journals, 71% of which are open access. Among related journals, the IET Computer Vision cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.