Detection of 3D Object in Point Cloud: Cloud Semantic Segmentation in Lane Marking
Article Sidebar
Main Article Content
Abstract
Managing a city efficiently and effectively is more important than ever as growing population and economic strain put a strain on infrastructure like transportation and public services like keeping urban green areas clean and maintained. For effective administration, knowledge of the urban setting is essential. Both portable and stationary laser scanners generate 3D point clouds that accurately depict the environment. These data points may be used to infer the state of the roads, buildings, trees, and other important elements involved in this decision-making process. Perhaps they would support "smart" or "smarter" cities in general. Unfortunately, the point clouds do not immediately supply this sort of data. It must be eliminated. This extraction is done either by human specialists or by sophisticated computer programmes that can identify objects. Because the point clouds might represent such large locations, relying on specialists to identify the things may be an unproductive use of time (streets or even whole cities). Automatic or nearly automatic discovery and recognition of essential objects is now possible with the help of object identification software. In this research, In this paper, we describe a unique approach to semantic segmentation of point clouds, based on the usage of contextual point representations to take use of both local and global features within the point cloud. We improve the accuracy of the point's representation by performing a single innovative gated fusion on the point and its neighbours, which incorporates the knowledge from both sets of data and enhances the representation of the point. Following this, we offer a new graph point net module that further develops the improved representation by composing and updating each point's representation inside the local point cloud structure using the graph attention block in real time. Finally, we make advantage of the global structure of the point cloud by using spatial- and channel-wise attention techniques to construct the ensuing semantic label for each point.
Article Details
References
Iro Armeni, Ozan Sener, Amir R Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese. 3D semantic parsing of large-scale indoor spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1534–1543, 2016.
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5828–5839, 2017.
Francis Engelmann, Theodora Kontogianni, Alexander Hermans, and Bastian Leibe. Exploring spatial context for 3D semantic segmentation of point clouds. In Proceedings of the IEEE International Conference on Computer Vision, pages 716–724, 2017.
Jun Fu, Jing Liu, Haijie Tian, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3146–3154, 2019.
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 3D semantic segmentation with submanifold sparse convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3577–3586, 2018.
Qiangui Huang, Weiyue Wang, and Ulrich Neumann. Recurrent slice networks for 3D segmengtation of point clouds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2626–2635, 2018.
Roman Klokov and Victor Lempitsky. Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models. In Proceedings of the IEEE International Conference on Computer Vision, pages 863–872, 2017.
Loic Landrieu and Martin Simonovsky. Large-scale point cloud semantic segmentation with superpoint graphs. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4558–4567, 2018.
Truc Le and Ye Duan. Pointgrid: A deep network for 3D shape understandings. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 9204–9214, 2018.
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. PointCNN: Convolution on X-transformed points. In Advances in Neural Information Processing Systems, pages 820–830, 2018.
Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan. Relation-shape convolutional neural network for point cloud analysis. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8895–8904, 2019.
Guan Pang and Ulrich Neumann. 3D point cloud object detection with multi-view convolutional neural network. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 585–590. IEEE, 2016.
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.
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in Neural Information Processing Systems, pages 5099–5108, 2017.
Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, and Raquel Urtasun. 3D graph neural networks for RGBD semantic segmentation. In International Conference on Computer Vision (ICCV), pages 5199–5208, 2017.
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger. Octnet: Learning deep 3D representations at high resolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3577–3586, 2017.
Ji Hou, Angela Dai, and Matthias Nießner. 3d-sis: 3d semantic instance segmentation of rgb-d scans. In CVPR, 2019.
Paul VC Hough. Machine analysis of bubble chamber pictures. In Conf. Proc., 1959.
Vadetay Saraswathi Bai, T. Sudha. (2023). A Systematic Literature Review on Cloud Forensics in Cloud Environment. International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 565–578. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2727
Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T Barron, Mario Fritz, Kate Saenko, and Trevor Darrell. A category-level 3d object dataset: Putting the kinect to work. In Consumer depth cameras for computer vision. 2013.
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017.
Jan J Koenderink and Andrea J Van Doorn. Affine structure from motion. JOSA A, 8(2):377–385, 1991.
Jason Ku, Melissa Mozifian, Jungwook Lee, Ali Harakeh, and Steven L Waslander. Joint 3d proposal generation and object detection from view aggregation. In IROS. IEEE, 2018.
Jean Lahoud and Bernard Ghanem. 2d-driven 3d object detection in rgb-d images. In CVPR, pages 4622–4630, 2017.
Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In CVPR, 2019.
Chen Wang, Danfei Xu, Yuke Zhu, Roberto Mart´?n-Mart´?n, Cewu Lu, Li Fei-Fei, and Silvio Savarese. Densefusion: 6d object pose estimation by iterative dense fusion. In CVPR, pages 3343–3352, 2019.
Weiyao Wang, Du Tran, and Matt Feiszli. What makes training multi-modal networks hard? arXiv preprint arXiv:1905.12681, 2019.
Carlos Silva, David Cohen, Takashi Yamamoto, Maria Petrova, Ana Costa. Ethical Considerations in Machine Learning Applications for Education. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/192
Jianxiong Xiao, Andrew Owens, and Antonio Torralba. Sun3d: A database of big spaces reconstructed using sfm and object labels. In ICCV. IEEE, 2013.
Danfei Xu, Dragomir Anguelov, and Ashesh Jain. Pointfusion: Deep sensor fusion for 3d bounding box estimation. In CVPr, pages 244–253, 2018.
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong. O-CNN: Octree-based convolutional neural networks for 3D shape analysis. ACM Transactions on Graphics (TOG), 36(4):72, 2017.
Weiyue Wang, Ronald Yu, Qiangui Huang, and Ulrich Neumann. SGPN: Similarity group proposal network for 3D point cloud instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2569–2578, 2018.
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. Dynamic graph CNN for learning on point clouds. ACM Transactions on Graphics (TOG), 2019.
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 3D shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912–1920, 2015.
Saining Xie, Sainan Liu, Zeyu Chen, and Zhuowen Tu. Attentional shapecontextnet for point cloud recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4606–4615, 2018.
Mark White, Thomas Wood, Maria Hernandez, María González , María Fernández. Enhancing Learning Analytics with Machine Learning Techniques. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/184
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.