Analysis of Current Advancement in 3D Point Cloud Semantic Segmentation
DOI:
https://doi.org/10.4108/eetiot.4495Keywords:
Point cloud, Semantic segmentation, Datasets, Deep learningAbstract
INTRODUCTION: The division of a 3D point cloud into various meaningful regions or objects is known as point cloud segmentation.
OBJECTIVES: The paper discusses the challenges faced in 3D point cloud segmentation, such as the high dimensionality of point cloud data, noise, and varying point densities.
METHODS: The paper compares several commonly used datasets in the field, including the ModelNet, ScanNet, S3DIS, and Semantic 3D datasets, ApploloCar3D, and provides an analysis of the strengths and weaknesses of each dataset. Also provides an overview of the papers that uses Traditional clustering techniques, deep learning-based methods, and hybrid approaches in point cloud semantic segmentation. The report also discusses the benefits and drawbacks of each approach.
CONCLUSION: This study sheds light on the state of the art in semantic segmentation of 3D point clouds.
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References
Chen, X.. Ma, H.. Wan, J.. Li, B.. Xia, T.: Multi-view 3D object detection network for au- tonomous driving. CVPR, (2017). DOI: https://doi.org/10.1109/CVPR.2017.691
Juana, V. H., Abhinav, V.: Chapter 12 - Semantic scene segmentation for robotics, Editor(s): Alexandros Iosifidis, Anastasios Tefas, Deep Learning for Robot Perception and Cognition. Academic Press, 279-311, (2022). DOI: https://doi.org/10.1016/B978-0-32-385787-1.00017-8
Huang, X., Mei, G., Zhang, J., Abbas, R.: A comprehensive survey on point cloud registra- tion, arXiv:2103.02690, (2021).
Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3D point clouds: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43(12), 4338–4364 (2021). DOI: https://doi.org/10.1109/TPAMI.2020.3005434
Lu, H., Shi, H.: Deep learning for 3D point cloud understanding: A survey. arXiv:2009.08920, (2020).
Nguyen, A., Le, B.: 3D point cloud segmentation: A survey. 6th IEEE Conference on Ro- botics, Automation and Mechatronics (RAM), pp. 225-230, Philippines, (2013). DOI: https://doi.org/10.1109/RAM.2013.6758588
Garcia, G. A., O-Escolano, S., Oprea, S., Martinez, V.V., Rodriguez, G. J.: A Review on Deep Learning Techniques Applied to Semantic Segmentation. https://arxiv.org/abs/1704.06857, (2017).
Dataset http://3dshapenets.cs.princeton.edu/
Ibrahim, M., Akhtar, N., Wise, M., Mian, M.: PC-Urban Outdoor dataset for 3D point Cloud semantic segmentation. IEEE Dataport, (2021). DOI: https://doi.org/10.1109/ACCESS.2021.3062547
Dataset https://www.semantic3d.net/
Dataset http://3Dsemantics.stanford.edu/.
Dataset www.semantic-kitti.org.
Dataset https://aihabitat.org/datasets/hm3dsemantics/
Dataset https://apolloscape.auto/car_instance.html
Gould, S., Fulton, R., Koller, D.: Decomposing a Scene into Geometric and Semantically Consistent Regions. Proceedings of the IEEE International Conference on Computer Vision pp. 1-8, (2009). DOI: https://doi.org/10.1109/ICCV.2009.5459211
Ouali, Y., Hudelot, C., Tami, M.: Semi-Supervised Semantic Segmentation with Cross-Con- sistency Training, arxiv.org/pdf/2003.09005, CVPR, (2020). DOI: https://doi.org/10.1109/CVPR42600.2020.01269
Lee, D-H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on Challenges in Representation Learning, ICML, vol. 3, pp. 2, (2013).
Dai, J., He, K., Sun, J.: BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation. 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1635-1643, Santiago, Chile, (2015). DOI: https://doi.org/10.1109/ICCV.2015.191
Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic segmentation using gener- ative adversarial network. In Proceedings of the IEEE International Conference on Com- puter Vision, pp. 5688–5696, (2017). DOI: https://doi.org/10.1109/ICCV.2017.606
Hung, W.C., Yi-Hsuan, T., Yan-Ting, L., Yen-Yu, L., Ming-Hsuan, Y.: Adversarial learn- ing for semi-supervised semantic segmentation. arXiv:1802.07934, (2018).
Wei, Y., Xiao, H., Shi, H., Jie, Z., Feng, J., S- Huang, T.: Revisiting dilated convolution: A simple approach for weakly- and semi-supervised semantic segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018). DOI: https://doi.org/10.1109/CVPR.2018.00759
Lee, J., Kim, E., Lee, S., Lee, J., Yoon, S.: Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5267–5276, (2019). DOI: https://doi.org/10.1109/CVPR.2019.00541
French, G., Aila, T., Laine, S., Mackiewicz, M., Finlayson, G.: Consistency regularization and cutmix for semi-supervised semantic segmentation. arXiv preprint arXiv:1906.01916, (2019).
Song,C., Huang, Y., Ouyang, W., Wang, L.: Box-driven class-wise region masking and fill- ing rate guided loss for weakly supervised semantic segmentation, 2019 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pp. 3131-31402019, (2019). DOI: https://doi.org/10.1109/CVPR.2019.00325
Lingni, M., Stückler, J., Kerl, C., Cremers, D.: Multi-view deep learning for consistent se- mantic mapping with RGB-D cameras. pp. 598-605, (2017).
Sellami, A., Tabbone, S.: Video semantic segmentation using deep multi-view representa- tion learning. 25th International Conference on Pattern Recognition (ICPR), pp. 1-7, Milan, Italy, (2021). DOI: https://doi.org/10.1109/ICPR48806.2021.9413239
Poux, F., Billen, R. Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods. ISPRS Int. J. Geo-Inf. 8(213), (2019). DOI: https://doi.org/10.3390/ijgi8050213
Liu, Z., Tang, H., Y-L. Song, H.: Point-Voxel CNN for Efficient 3D Deep Learning, arxiv.org/abs/1907.03739v2, (2019).
Charles, R., Qi, Su, H., Mo, K., J.-Guibas, L.; PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 652-660, (2017). DOI: https://doi.org/10.1109/CVPR.2017.16
Abdul, N., Teferle, N., Li, J., Lindenbergh, R., Parvaz, S.: Investigation of PointNet for Semantic Segmentation of Large-Scale Outdoor Point Clouds. ISPRS, (2021).
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep hierarchical feature learning on point sets in a metric space, NeurIPS, (2017).
Turgut, K., Dutagaci, H., Galopin, G.: Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods. Plant Methods. 18(20), (2022). DOI: https://doi.org/10.1186/s13007-022-00857-3
Wu, W., Qi, Z., Fuxin, L.: PointConv: Deep Convolutional Networks on 3D Point Clouds, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9613-9622, CA, USA, (2019). DOI: https://doi.org/10.1109/CVPR.2019.00985
Yuan, W., Point Cloud Semantic Segmentation using Graph Convolutional Network.
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M. M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. ACM TOG, (2019). DOI: https://doi.org/10.1145/3326362
Liang, Z., Yang, M., Deng, L., Wang, C., Wang, B.: Hierarchical Depthwise Graph Convo- lutional Neural Network for 3D Semantic Segmentation of Point Clouds. 2019 International Conference on Robotics and Automation (ICRA), pp. 8152-8158, Canada, (2019). DOI: https://doi.org/10.1109/ICRA.2019.8794052
Zhang, F., Fang, J., Wah, B., Torr, P.: Deep FusionNet for Point Cloud Semantic Segmen- tation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12369. Springer, Cham. (2020). DOI: https://doi.org/10.1007/978-3-030-58586-0_38
Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with super- point graphs in Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pp. 4558– 4567, (2018). DOI: https://doi.org/10.1109/CVPR.2018.00479
Jiang, L., Zhao, H., Liu, S., Shen, X., Fu, C-H., Jia, J.: Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10432-10440, (2019). DOI: https://doi.org/10.1109/ICCV.2019.01053
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