The DOI matches its Crossref record; 203 of the 217 references it cites were found in a registry or an index.
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Abstract
3D semantic segmentation is a fundamental task for robotic and autonomous driving applications. Recent works have been focused on using deep learning techniques, whereas developing fine-annotated 3D LiDAR datasets is extremely labor intensive and requires professional skills. The performance limitation caused by insufficient datasets is called data hunger problem. This research provides a comprehensive survey on the question: are we hungry for 3D LiDAR data for semantic segmentation? The studies are conducted at three levels. First, a broad review to the main 3D LiDAR datasets is conducted, followed by a statistical analysis on three representative datasets to gain an in-depth view on the datasets’ size, diversity and quality, which are the critical factors in learning deep models. Second, an organized survey of 3D semantic segmentation methods is given with a focus on the mainstream of the latest research trend using deep learning techniques, followed by a systematic survey to the existing efforts to solve the data hunger problem. Finally, an insightful discussion of the remaining problems on both methodological and datasets’ viewpoints, and the open questions on dataset bias, domain and semantic gap are given, leading to potential topics in future works. To the best of our knowledge, this is the first work to study the data hunger problem for 3D semantic segmentation using deep learning techniques, which are addressed in both methodological and dataset review, and we share findings and discussions through a comprehensive dataset analysis.
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“However, the pointclouds from these sensors are relatively sparse and lack color information, which results in a significant classification error in fine-grained segmentation [42].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [42]
Citations
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Flagged first, then by citations
14 of the 217 references couldn’t be matched to a known paper.
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Items 101–104 of 104
TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understanding
Nguyen, D. V., Lai, Y.-L., Zhang, Q., Gyawali, P., Wang, F., Schwab, B., Wysocki, O., & Kolbe, T. H. (2026) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv69130.2026.00180
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Efficient semantic segmentation for 3D Gaussian Splatting via confidence-based label assignment
Velasteguí, R., Rauch, C., Karaoğlu, S., Pfingsthorn, M. F., & Gevers, T. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104943
Metadata
- Authors
- Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H.
- Affiliations
- Peking University (CN)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-07
- Volume
- 23
- Issue
- 7
- Pages
- 6063-6081
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3160425077
- Cited by
- 105
- References
- 217
- Citations by year
- 2026: 13 · 2025: 26 · 2024: 19 · 2023: 27 · 2022: 19
- FWCI
- 5.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Development Program of China (2017YFB1002601)
- National Natural Science Foundation of China (61973004)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- 3D semantic segmentation, 3D LiDAR, dataset quality, data bias, domain gap, semantic gap, autonomous driving, robotics, deep learning
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