GCN-Net: 3D Point Cloud Classification & Localization Using Graph-CNN
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Abstract
In this paper, we have demonstrated the application of a graph convolutional neural network for the purpose of object detection in a LiDAR point cloud. In order to encode the point cloud in the most time-effective manner, we make use of a near-neighbors graph with a defined radius. We create a graph convolutional neural network so that we can find out what kind of object and what class each vertex in a graph represents. We design a box merging and scoring operation to reliably combine detections from numerous vertices into a single score, and we offer an auto-registration strategy as a means of reducing the amount of translation errors that occur inside the system. According to the results of our tests using the KITTI benchmark, we are able to draw the conclusion that the method that was suggested achieves competitive accuracy with the point cloud, even beating fusion-based methods in some instances. According to the results of our research, the graph neural network has the potential to become an effective new tool for the detection of 3D objects.
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How to Cite
[1]
A. Abdullah and M. H. Nahid, “GCN-Net: 3D Point Cloud Classification & Localization Using Graph-CNN”, AJSE, vol. 23, no. 1, pp. 11- 16, Apr. 2024.
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[3] Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai, et al. Recent advances in convolutional neural networks. Pattern recognition, 77:354–377, 2018.
[4] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4490–4499, 2018.
[5] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652–660, 2017.
[6] Yang Ye, Xiulong Yang, and Shihao Ji. Apsnet: Attention based point cloud sampling. ArXiv preprint arXiv:2210.05638, 2022.
[7] Florent Lafarge and Cl´ement Mallet. Creating large-scale city models from 3d-point clouds: a robust approach with hybrid representation. International journal of computer vision, 99:69– 85, 2012.
[8] Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231–1237, 2013.
[9] Bin Yang, Wenjie Luo, and Raquel Urtasun. Pixor: Real-time 3d object detection from point clouds. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pages 7652–7660, 2018.
[10] Zining Wang, Wei Zhan, and Masayoshi Tomizuka. Fusing bird’s eye view lidar point cloud and front view camera image for 3d object detection. In 2018 IEEE intelligent vehicles symposium (IV), pages 1–6. IEEE, 2018.
[11] Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, and Ingmar Posner. Vote3deep: Fast object detection in 3d point clouds using efficient convolutional neural networks. In 2017 IEEE Internationa Conference on Robotics and Automation (ICRA), pages 1355–1361. IEEE, 2017.
[12] Yan Zhang, Jonathon Hare, and Adam Prugel-Bennett. Deep set prediction networks. Advances in Neural Information Processing Systems, 32, 2019.
[13] Matt W Gardner and SR Dorling. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15):2627– 2636, 1998.
[14] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30, 2017.
[15] Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas. Frustum pointnets for 3d object detection from rgb-d data. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 918– 927, 2018.
[16] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointrcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770– 779, 2019.
[17] Charles R Qi, Yin Zhou, Mahyar Najibi, Pei Sun, Khoa Vo, Boyang Deng, and Dragomir Anguelov. Offboard 3d object detection from point cloud sequences. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6134–6144, 2021.
[18] Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12697–12705, 2019.
[19] Xiaojun Wan, Jianwu Yang, and Jianguo Xiao. Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction. In Proceedings of the 45th annual meeting of the association of computational linguistics, pages 552–559, 2007.
[20] Weijing Shi and Raj Rajkumar. Point-gnn: Graph neural network for 3d object detection in a point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1711–1719, 2020.
[21] Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L Waslander. Joint 3d proposal generation and object detection from view aggregation. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1–8. IEEE, 2018.
[22] Qian Wang and Min-Koo Kim. Applications of 3d point cloud data in the construction industry: A fifteen-year review from 2004 to 2018. Advanced Engineering Informatics, 39:306–319, 2019.
[23] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li. Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10529–10538, 2020.
[24] Zetong Yang, Yanan Sun, Shu Liu, Xiaoyong Shen, and Jiaya Jia. Std: Sparse-to-dense 3d object detector for point cloud. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1951–1960, 2019