Abstract
The three-dimensional data is the core tool behind environment aware algorithms used in e.g. SLAM or autonomous driving. As a data format, point clouds are becoming increasingly popular, due to their high-resolution and mapping fidelity. However, representing data as points, rather than voxels, comes with very high processing complexity, as machine learning models need to deal with permutation-invariance within samples. The PointNet architecture provides an easy and efficient way to deal with the point cloud data, by performing feature extraction for each point separately and then computing feature-wise max function. In this work, we present a comparison of different permutation-invariant functions used for this aggregation evaluated on the ShapeNet dataset for the classification task.
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References
Achlioptas, P., Diamanti, O., Mitliagkas, I., Guibas, L.: Learning representations and generative models for 3D point clouds. In: International Conference on Machine Learning, pp. 40–49 (2018)
Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)
Kanezaki, A., Matsushita, Y., Nishida, Y.: Rotationnet: joint object categorization and pose estimation using multiviews from unsupervised viewpoints. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5010–5019 (2018)
Li, J., Chen, B.M., Hee Lee, G.: SO-Net: self-organizing network for point cloud analysis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Maturana, D., Scherer, S.: VoxNet: a 3D convolutional neural network for real-time object recognition. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 922–928. IEEE (2015)
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum PointNets for 3D object detection from RGB-D data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 918–927 (2018)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. CoRR abs/1706.02413v1 (2017). http://arxiv.org/abs/1706.02413v1
Stypułkowski, M., Zamorski, M., Zięba, M., Chorowski, J.: Conditional invertible flow for point cloud generation. arXiv preprint arXiv:1910.07344 (2019)
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV 2015, pp. 945–953. IEEE Computer Society, Washington (2015). https://doi.org/10.1109/ICCV.2015.114
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-CNN: octree-based convolutional neural networks for 3D shape analysis. ACM Trans. Graph. (TOG) 36(4), 72 (2017)
Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912–1920 (2015)
Yang, G., Huang, X., Hao, Z., Liu, M.Y., Belongie, S., Hariharan, B.: PointFlow: 3D point cloud generation with continuous normalizing flows. arXiv preprint arXiv:1906.12320 (2019)
Zamorski, M., et al.: Adversarial autoencoders for compact representations of 3D point clouds. arXiv preprint arXiv:1811.07605 (2018)
Zhou, Y., Tuzel, O.: VoxelNet: end-to-end learning for point cloud based 3D object detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
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Zamorski, M., Zięba, M., Świątek, J. (2020). Comparison of Aggregation Functions for 3D Point Clouds Classification. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_43
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DOI: https://doi.org/10.1007/978-3-030-41964-6_43
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