Method for augmenting 3D point cloud models using graph neural networks
DOI:
https://doi.org/10.30837/2522-9818.2025.2.129Keywords:
point cloud; deep learning; graph neural network; automatic point cloud completion; graph encoding; 3D reconstruction of graph models.Abstract
Constructing models representing three-dimensional objects as a set of unstructured points (3D point cloud models) in space is becoming increasingly widespread across various domains, including autonomous navigation, robotics, virtual/augmented reality, and 3D reconstruction. Accurately capturing and processing 3D point cloud data is critical for applications requiring a comprehensive understanding of the surrounding environment, such as obstacle avoidance, path planning, and scene modelling. However, due to various reasons, point clouds often contain missing regions, posing significant challenges for subsequent data processing. Incomplete point cloud data can have serious consequences, for instance, in autonomous navigation systems, where errors may lead to collisions or other hazardous situations. Addressing this issue is crucial for the reliable processing of 3D data. This work aims to develop and investigate a method for automatically completing and reconstructing point clouds using graph neural networks. The study's primary objectives include analysing existing approaches to constructing and restoring three-dimensional graph models, developing and implementing a method for automatically completing point clouds using graph neural networks and modelling the proposed method for tasks related to the completion and 3D reconstruction of point cloud models. In this work, a conceptual model for point cloud completion was developed using graph neural networks, enabling the efficient encoding of incomplete point clouds as graphs and the prediction of missing points. The proposed solution for completing incomplete 3D point clouds offers scientific novelty and combines the power of graph neural networks (GNN) with the Point Completion Network (PCN) architecture. The suggested approach allows for high-quality restoration of incomplete 3D data, essential for numerous applications, such as 3D reconstruction, robot navigation, and more. The practical significance of the work’s results is validated by the modelling outcomes of the developed method on classical datasets and their comparison with existing approaches to solving the studied problem. A promising direction for further research on this topic includes testing various architectures of graph neural networks, tuning hyperparameters, applying alternative loss functions, and leveraging more powerful computational resources to train the constructed neural network models.
References
Список літератури
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Hammond D. K., Vandergheynst P., Gribonval R. Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis. 2011. Vol. 30, no. 2. P. 129–150. DOI: https://doi.org/10.1016/j.acha.2010.04.005
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References
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Sung, M. (2015), "Data-driven structural priors for shape completion" / M. Sung et al. ACM Transactions on Graphics. 2015. Vol. 34, No. 6. P. 1–11. DOI: https://doi.org/10.1145/2816795.2818094
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Nguyen, D. T. (2016), "A Field Model for Repairing 3D Shapes" / D. T. Nguyen et al. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016. DOI: https://doi.org/10.1109/cvpr.2016.612
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Kalogerakis, E. (2012), "A probabilistic model for component-based shape synthesis" / E. Kalogerakis et al. ACM Transactions on Graphics. 2012. Vol. 31, No. 4. P. 1–11. DOI: https://doi.org/10.1145/2185520.2185551
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Liu, M. (2020), "Morphing and Sampling Network for Dense Point Cloud Completion" / M. Liu et al. Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Vol. 34, No. 07. P. 11596–11603. DOI: https://doi.org/10.1609/aaai.v34i07.6827
Lawrance, N. R. J., Chung J. J., Hollinger G. A. (2017), "Fast Marching Adaptive Sampling". IEEE Robotics and Automation Letters. 2017. Vol. 2, No. 2. P. 696–703. DOI: https://doi.org/10.1109/lra.2017.2651148
Chen, X., Chen, B., Mitra, N. J. (2020),"Unpaired Point Cloud Completion on Real Scans using Adversarial Training". International Conference On Learning Representations (ICLR 2020). DOI: https://doi.org/10.48550/arXiv.1904.00069
"Generative Adversarial Networks / I. J. Goodfellow et al. Advances in Neural Information Processing Systems". 2014. URL: https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf. (last accessed: 15.05.2024).
Huang, Z. (2020), "PF-Net: Point Fractal Network for 3D Point Cloud Completion" / Z. Huang et al. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13–19 June 2020. DOI: https://doi.org/10.1109/cvpr42600.2020.00768
Mendoza, A. (2020), "Refinement of Predicted Missing Parts Enhance Point Cloud Completion" / A. Mendoza et al. Computer Vision and Pattern Recognition. 11 р. DOI: https://doi.org/10.48550/arXiv.2010.04278
Spurek, P. (2022), "HyperPocket: Generative Point Cloud Completion" / P. Spurek et al. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23–27 October 2022. DOI: https://doi.org/10.1109/iros47612.2022.9981829
Ha, D., Dai, A., Le, Q. V. (2022), "Hypernetworks. International Conference on Learning Representa- tions". Machine Learning. 2022. DOI: https://doi.org/10.48550/arXiv.1609.09106
Xiang, P. (2021), "SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer" / P. Xiang et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10–17 October 2021. DOI: https://doi.org/10.1109/iccv48922.2021.00545
Pan, L. (2020), "ECG: Edge-aware Point Cloud Completion with Graph Convolution". IEEE Robotics and Automation Letters. 2020. Vol. 5, No. 3. P. 4392–4398. DOI: https://doi.org/10.1109/lra.2020.2994483
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Wu, Y. (2020), "Graph Convolutional Networks with Markov Random Field Reasoning for Social Spammer Detection" / Y. Wu et al. Proceedings of the AAAI Conference on Artificial Intelligence. 2020. Vol. 34, No. 01. P. 1054–1061. DOI: https://doi.org/10.1609/aaai.v34i01.5455
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Perozzi, B., Al-Rfou, R., Skiena, S. S. (2014), "DeepWalk: online learning of social representations". KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 20th ed. 2014. DOI: https://doi.org/10.1145/2623330.2623732
Mallat, S. Wavelet Tour of Signal Processing. Elsevier Science & Technology Books, 1999.
Hammond, D. K., Vandergheynst, P., Gribonval, R. (2011), "Wavelets on graphs via spectral graph theory". Applied and Computational Harmonic Analysis. 2011. Vol. 30, No. 2. P. 129–150. DOI: https://doi.org/10.1016/j.acha.2010.04.005
Kipf, T. N., Welling, M. (2016), "Semi-Supervised Classification with Graph Convolutional Networks". Machine Learning. 2016. DOI: https://doi.org/10.48550/arXiv.1609.02907
Hamilton, W. L., Ying, R., Leskovec, J. (2017), "Inductive Representation Learning on Large Graphs". Social and Information Networks. 2017. DOI: https://doi.org/10.48550/arXiv.1706.02216
Wang, Y. (2019), "Dynamic Graph CNN for Learning on Point Clouds" / Y. Wang et al. ACM Transactions on Graphics. 2019. Vol. 38, No. 5. P. 1–12. DOI: https://doi.org/10.1145/3326362
Yang, Y. (2018), "FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation" / Y. Yang et al. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, 18–23 June 2018. DOI: https://doi.org/10.1109/cvpr.2018.00029
Cai, P. (2024), "Orthogonal Dictionary Guided Shape Completion Network for Point Cloud" / P. Cai et al. Proceedings of the AAAI Conference on Artificial Intelligence. 2024. Vol. 38, No. 2. P. 864–872. DOI: https://doi.org/10.1609/aaai.v38i2.27845
Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., and Sutskever, I. (2019), "Deep double descent: Where bigger models and more data hurt". Machine Learning. DOI: https://doi.org/10.48550/arXiv.1912.02292
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