The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index.
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
Open accessAbstract
Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.
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He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Fully convolutional networks for semantic segmentation
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Cited by
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105
Understanding the Challenges When 3D Semantic Segmentation Faces Class Imbalanced and OOD Data
Pan, Y., Xie, F., & Zhao, H. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3256442 · Cited by 17
Metadata
- Authors
- Xie, Y., Tian, J., & Zhu, X. X.
- Venue
- IEEE Geoscience and Remote Sensing Magazine
- Published
- 2020-12
- Volume
- 8
- Issue
- 4
- Pages
- 38-59
- ISSN
- 2168-6831 (ISSN-L)
- 2473-2397
- 2373-7468
- OpenAlex
- W3041049917
- Cited by
- 482
- References
- 122
- FWCI
- 39.53
- Type
- journal article
- Language
- en
- Open access
- green
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