The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion
Open accessAbstract
LiDAR point cloud analysis is a core task for 3D computer vision, especially for autonomous driving. However, due to the severe sparsity and noise interference in the single sweep LiDAR point cloud, the accurate semantic segmentation is non-trivial to achieve. In this paper, we propose a novel sparse LiDAR point cloud semantic segmentation framework assisted by learned contextual shape priors. In practice, an initial semantic segmentation (SS) of a single sweep point cloud can be achieved by any appealing network and then flows into the semantic scene completion (SSC) module as the input. By merging multiple frames in the LiDAR sequence as supervision, the optimized SSC module has learned the contextual shape priors from sequential LiDAR data, completing the sparse single sweep point cloud to the dense one. Thus, it inherently improves SS optimization through fully end-to-end training. Besides, a Point-Voxel Interaction (PVI) module is proposed to further enhance the knowledge fusion between SS and SSC tasks, i.e., promoting the interaction of incomplete local geometry of point cloud and complete voxel-wise global structure. Furthermore, the auxiliary SSC and PVI modules can be discarded during inference without extra burden for SS. Extensive experiments confirm that our JS3C-Net achieves superior performance on both SemanticKITTI and SemanticPOSS benchmarks, i.e., 4% and 3% improvement correspondingly.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368
KPConv: Flexible and Deformable Convolution for Point Clouds
Thomas, H., Qi, C. R., Deschaud, J.-E., Marcotegui, B., Goulette, F., & Guibas, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,212
Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Cipolla, R., Gal, Y., & Kendall, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00781 · Cited by 3,151
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00593 · Cited by 2,817
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00939 · Cited by 2,294
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 2,114
Cited by
Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction
Huang, Y., Zheng, W., Zhang, Y., Zhou, J., & Lu, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00890 · Cited by 312
MonoScene: Monocular 3D Semantic Scene Completion
Cao, A.-Q., & de Charette, R. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00396 · Cited by 293
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
Yan, X., Gao, J., Zheng, C., Zheng, C., Zhang, R., Cui, S., & Li, Z. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_39 · Cited by 280
Self-citationOpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception
Wang, X., Zhu, Z., Xu, W., Zhang, Y., Wei, Y., Chi, X., Ye, Y., Du, D., Lu, J., & Wang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01636 · Cited by 192
OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction
Ming, Z., Stephany Berrio, J., Shan, M., & Worrall, S. (2025) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3453293 · Cited by 35
Metadata
- Authors
- Yan, X., Gao, J., Li, J., Zhang, R., Li, Z., Huang, R., & Cui, S.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2021-05-18
- Volume
- 35
- Issue
- 4
- Pages
- 3101-3109
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W3174692508
- Cited by
- 282
- References
- 50
- FWCI
- 34.37
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Yan, X., Gao, J., Li, J., Zhang, R., Li, Z., Huang, R., & Cui, S. (2021). Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v35i4.16419
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