Abstract
In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification. An ensemble of multiple model instances is known to outperform a single model instance, but there is little study of the topic of ensemble learning for 3D point clouds. First, an ensemble of multiple model instances trained on the same part of the ModelNet40 dataset was tested for seven deep learning, point cloud-based classification algorithms: PointNet, PointNet++, SO-Net, KCNet, DeepSets, DGCNN, and PointCNN. Second, the ensemble of different architectures was tested. Results of our experiments show that the tested ensemble learning methods improve over state-of-the-art on the ModelNet40 dataset, from 92.65% to 93.64% for the ensemble of single architecture instances, 94.03% for two different architectures, and 94.15% for five different architectures. We show that the ensemble of two models with different architectures can be as effective as the ensemble of 10 models with the same architecture. Third, a study on classic bagging (i.e. with different subsets used for training multiple model instances) was tested and sources of ensemble accuracy growth were investigated for best-performing architecture, i.e. SO-Net. We measure the inference time of all 3D classification architectures on a Nvidia Jetson TX2, a common embedded computer for mobile robots, to allude to the use of these models in real-life applications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arvind, V., Costa, A., Badgeley, M., Cho, S., Oermann, E.: Wide and deep volumetric residual networks for volumetric image classification. arXiv preprint arXiv:1710.01217 (2017)
Chang, A.X., et al.: ShapeNet: an information-rich 3D model repository. Technical report. arXiv:1512.03012 [cs.GR], Stanford University – Princeton University – Toyota Technological Institute at Chicago (2015)
Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: ScanNet: richly-annotated 3D reconstructions of indoor scenes (2017). arxiv:1702.04405
Gan, Y., Tang, Y., Zhang, Q.: 3D model retrieval method based on mesh segmentation. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 8334, p. 120 (2012). https://doi.org/10.1117/12.961239
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Rob. Res. (IJRR) (2013)
Himmelsbach, M., Luettel, T., Wuensche, H.J.: Real-time object classification in 3D point clouds using point feature histograms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 994–1000. IEEE (2009)
Li, J., Chen, B.M., Lee, G.H.: SO-Net: self-organizing network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9397–9406 (2018)
Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31, pp. 828–838. Curran Associates, Inc. (2018)
Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings Computer Vision and Pattern Recognition (CVPR), vol. 1, no. 2, p. 4. IEEE (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, pp. 5099–5108 (2017)
Rokach, L.: Ensemble-based classifiers. Artif. Intell. Rev. 33(1–2), 1–39 (2010)
Rutzinger, M., Höfle, B., Hollaus, M., Pfeifer, N.: Object-based point cloud analysis of full-waveform airborne laser scanning data for urban vegetation classification. Sensors 8(8), 4505–4528 (2008)
Sfikas, K., Pratikakis, I., Theoharis, T.: Ensemble of PANORAMA-based convolutional neural networks for 3D model classification and retrieval. Comput. Graph. 71, 208–218 (2018)
Shen, Y., Feng, C., Yang, Y., Tian, D.: Mining point cloud local structures by kernel correlation and graph pooling. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 4 (2018)
Song, S., Lichtenberg, S.P., Xiao, J.: SUN RGB-D: A RGB-D scene understanding benchmark suite. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 567–576. IEEE (2015)
Su, H., Maji, S., Kalogerakis, E., Learned-Miller, E.: Multi-view convolutional neural networks for 3D shape recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 945–953 (2015)
Su, J., Gadelha, M., Wang, R., Maji, S.: A deeper look at 3D shape classifiers. CoRR abs/1809.02560 (2018)
Tang, J., Ren, Y., Liu, S.: Real-time robot localization, vision, and speech recognition on Nvidia Jetson TX1. CoRR abs/1705.10945 (2017)
Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds. arXiv preprint arXiv:1801.07829 (2018)
Wu, Z., et al.: 3D ShapeNets: a deep representation for volumetric shapes. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912–1920, June 2015. https://doi.org/10.1109/CVPR.2015.7298801
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Advances in Neural Information Processing Systems, pp. 3391–3401 (2017)
Acknowledgements
This research was partially supported by the Dean of Faculty of Mechatronics (Grant No. 504/03731 and Grant No. 504/03272). We want to thank authors of all architectures for providing a public repository. We would also like to gratefully acknowledge the helpful comments and suggestions of Tomasz Trzciński and Robert Sitnik.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Koguciuk, D., Chechliński, Ł., El-Gaaly, T. (2019). 3D Object Recognition with Ensemble Learning—A Study of Point Cloud-Based Deep Learning Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-33723-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33722-3
Online ISBN: 978-3-030-33723-0
eBook Packages: Computer ScienceComputer Science (R0)