Abstract
Understanding environment around the vehicle is essential for automated driving technology. For this purpose, an omnidirectional LiDAR is used for obtaining surrounding information and point cloud based semantic segmentation methods have been proposed. However, these methods requires a time to acquire point cloud data and to process the point cloud, which causes the significant positional shift of objects on the practical application scenario. In this paper, we propose a 1D self-attention network (1D-SAN) for LiDAR-based point cloud semantic segmentation, which is based on the 1D-CNN for real-time pedestrian detection of an omnidirectional LiDAR data. Because the proposed method can sequentially process during data acquisition in a omnidirectional LiDAR, we can reduce the processing time and suppress the positional shift. Moreover, for improving segmentation accuracy, we use the intensity as an input data and introduce self-attention mechanism into the proposed method. The intensity enables to consider the object texture. The self-attention mechanism can consider the relationship between point clouds. The experimental results with the SemanticKITTI dataset show that the intensity input and the self-attention mechanism in the proposed method improves the accuracy. Especially, the self-attention mechanism contributes to improving the accuracy of small objects. Also, we show that the processing time of the proposed method is faster than the other point cloud segmentation methods.
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References
Alonso, I., Riazuelo, L., Montesano, L., Murillo, A.C.: 3D-MiniNet: learning a 2D representation from point clouds for fast and efficient 3D LiDAR semantic segmentation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2020)
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: Proceeding of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: IEEE Conference on Computer Vision and Pattern Recognition, p. 3 (2017)
Cortinhal, T., Tzelepis, G., Aksoy, E.E.: Salsanext: fast, uncertainty-aware semantic segmentation of lidar point clouds for autonomous driving (2020)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361 (2012)
Hu, Q., et al.: Randla-net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2020)
Kunisada, Y., Yamashita, T., Fujiyoshi, H.: Pedestrian-detection method based on 1D-CNN during LiDAR rotation. In: The 21st IEEE International Conference on Intelligent Transportation Systems (ITSC) (2018)
Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989)
Maturana, D., Scherer, S.: Voxnet: a 3D convolutional neural network for real-time object recognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 922–928 (2015)
Meyer, G.P., Laddha, A., Kee, E., Vallespi-Gonzalez, C., Wellington, C.K.: Lasernet: an efficient probabilistic 3D object detector for autonomous driving. In: CVPR, pp. 12677–12686. Computer Vision Foundation/IEEE (2019)
Milioto, A., Vizzo, I., Behley, J., Stachniss, C.: RangeNet++: fast and accurate LiDAR semantic segmentation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019)
Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vis. 38(1), 15–33 (2000)
Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3D object detection from RGB-D data. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 918–927 (2018)
Qi, C.R., Su, H., Kaichun, M., Guibas, L.J.: Pointnet: deep learning on point sets for 3D classification and segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 77–85 (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)
Simon, M., Milz, S., Amende, K., Gross, H.M.: Complex-yolo: real-time 3D object detection on point clouds. arXiv preprint arXiv:1803.06199 (2018)
Spinello, L., Luber, M., Arras, K.O.: Tracking people in 3D using a bottom-up top-down detector. In: IEEE Robotics and Automation Society, pp. 1304–1310 (2011)
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M.H., Kautz, J.: SPLATNet: sparse lattice networks for point cloud processing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2530–2539 (2018)
Tatarchenko*, M., Park*, J., Koltun, V., Zhou., Q.Y.: Tangent convolutions for dense prediction in 3D. In: CVPR (2018)
Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., Guibas, L.J.: Kpconv: flexible and deformable convolution for point clouds. In: Proceedings of the IEEE International Conference on Computer Vision (2019)
Wu, B., Wan, A., Yue, X., Keutzer, K.: Squeezeseg: convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud. In: ICRA (2018)
Wu, B., Zhou, X., Zhao, S., Yue, X., Keutzer, K.: Squeezesegv 2: improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud. In: ICRA (2019)
Xu, C., et al.: Squeezesegv3: spatially-adaptive convolution for efficient point-cloud segmentation. arXiv preprint arXiv:2004.01803 (2020)
Zhang, Y., et al.: Polarnet: an improved grid representation for online lidar point clouds semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020
Zhao, H., Jia, J., Koltun, V.: Exploring self-attention for image recognition. In: CVPR (2020)
Zhou, Y., Tuzel, O.: Voxelnet: end-to-end learning for point cloud based 3D object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4490–4499 (2018)
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Suzuki, T., Hirakawa, T., Yamashita, T., Fujiyoshi, H. (2022). 1D Self-Attention Network for Point Cloud Semantic Segmentation Using Omnidirectional LiDAR. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_19
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