Page 27 - Read Online
P. 27
Wu et al. Intell Robot 2022;2(2):10529 I http://dx.doi.org/10.20517/ir.2021.20 Page 125
REFERENCES
1. Qi CR, Su H, Mo K, Guibas LJ. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the
IEEE conference on computer vision and pattern recognition; 2017. pp. 652–60. DOI
2. Qi CR, Yi L, Su H, Guibas LJ. PointNet++: Deep hierarchical feature learning on point sets in a metric space. Advances in Neural
Information Processing Systems 2017;30. DOI
3. Krispel G, Opitz M, Waltner G, Possegger H, Bischof H. Fuseseg: Lidar point cloud segmentation fusing multimodal data. In: Proceed
ings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 2020. pp. 1874–83. DOI
4. Xu D, Anguelov D, Jain A. Pointfusion: Deep sensor fusion for 3d bounding box estimation. In: Proceedings of the IEEE conference
on computer vision and pattern recognition; 2018. pp. 244–53. DOI
5. Guo Y, Wang H, Hu Q, et al. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence
2020. DOI
6. Li Y, Ma L, Zhong Z, et al. Deep learning for lidar point clouds in autonomous driving: A review. IEEE Transactions on Neural Networks
and Learning Systems 2020;32:3412–32. DOI
7. Liu W, Sun J, Li W, Hu T, Wang P. Deep learning on point clouds and its application: A survey. Sensors 2019;19:4188. DOI
8. Ioannidou A, Chatzilari E, Nikolopoulos S, Kompatsiaris I. Deep learning advances in computer vision with 3d data: A survey. ACM
Computing Surveys (CSUR) 2017;50:1–38. DOI
9. Feng D, HaaseSchütz C, Rosenbaum L, et al. Deep multimodal object detection and semantic segmentation for autonomous driving:
Datasets, methods, and challenges. IEEE Transactions on Intelligent Transportation Systems 2020;22:1341–60. DOI
10. Wang Z, Wu Y, Niu Q. Multisensor fusion in automated driving: A survey. Ieee Access 2019;8:2847–68. DOI
11. Cui Y, Chen R, Chu W, et al. Deep learning for image and point cloud fusion in autonomous driving: A review. IEEE Transactions on
Intelligent Transportation Systems 2021. DOI
12. Chen X, Ma H, Wan J, Li B, Xia T. Multiview 3d object detection network for autonomous driving. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2017. pp. 1907–15. DOI
13. Xu J, Zhang R, Dou J, et al. RPVNet: a deep and efficient rangepointvoxel fusion network for LiDAR point cloud segmentation. In:
Proceedings of the IEEE/CVF International Conference on Computer Vision; 2021. pp. 16024–33. DOI
14. Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE conference on
computer vision and pattern recognition. IEEE; 2012. pp. 3354–61. DOI
15. De Deuge M, Quadros A, Hung C, Douillard B. Unsupervised feature learning for classification of outdoor 3d scans. In: Australasian
Conference on Robitics and Automation. vol. 2; 2013. p. 1. DOI
16. Uy MA, Pham QH, Hua BS, Nguyen T, Yeung SK. Revisiting point cloud classification: A new benchmark dataset and classification
model on realworld data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision; 2019. pp. 1588–97. DOI
17. Varney N, Asari VK, Graehling Q. DALES: a largescale aerial LiDAR data set for semantic segmentation. In: Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; 2020. pp. 186–87. DOI
18. Ye Z, Xu Y, Huang R, et al. Lasdu: A largescale aerial lidar dataset for semantic labeling in dense urban areas. ISPRS International
Journal of GeoInformation 2020;9:450. DOI
19. Li X, Li C, Tong Z, et al. Campus3d: A photogrammetry point cloud benchmark for hierarchical understanding of outdoor scene. In:
Proceedings of the 28th ACM International Conference on Multimedia; 2020. pp. 238–46. DOI
20. Tan W, Qin N, Ma L, et al. Toronto3D: a largescale mobile lidar dataset for semantic segmentation of urban roadways. In: Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; 2020. pp. 202–3. DOI
21. Riemenschneider H, BódisSzomorú A, Weissenberg J, Van Gool L. Learning where to classify in multiview semantic segmentation.
In: European Conference on Computer Vision. Springer; 2014. pp. 516–32. DOI
22. Chang A, Dai A, Funkhouser T, et al. Matterport3D: Learning from RGBD Data in Indoor Environments. In: 2017 International
Conference on 3D Vision (3DV). IEEE Computer Society; 2017. pp. 667–76. DOI
23. Patil A, Malla S, Gang H, Chen YT. The h3d dataset for fullsurround 3d multiobject detection and tracking in crowded urban scenes.
In: 2019 International Conference on Robotics and Automation. IEEE; 2019. pp. 9552–57. DOI
24. Chang MF, Lambert J, Sangkloy P, et al. Argoverse: 3d tracking and forecasting with rich maps. In: Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition; 2019. pp. 8748–57. DOI
25. Kesten R, Usman M, Houston J, et al. Lyft level 5 av dataset 2019. urlhttps://level5 lyft com/dataset 2019. Available from: https:
//level5.lyft.com/dataset.
26. Sun P, Kretzschmar H, Dotiwalla X, et al. Scalability in perception for autonomous driving: Waymo open dataset. In: Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. pp. 2446–54. DOI
27. Caesar H, Bankiti V, Lang AH, et al. nuscenes: A multimodal dataset for autonomous driving. In: Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition; 2020. pp. 11621–31. DOI
28. Qian K, Zhu S, Zhang X, Li LE. Robust multimodal vehicle detection in foggy weather using complementary lidar and radar signals. In:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2021. pp. 444–53. DOI
29. Zhang Z, Hua BS, Yeung SK. ShellNet:Efficient point cloud convolutional neural networks using concentric shells statistics. 2019
IEEE/CVF International Conference on Computer Vision 2019:1607–16. DOI
30. Komarichev A, Zhong Z, Hua J. ACNN: Annularly convolutional neural networks on point clouds. 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition 2019:7413–22. DOI
31. Wu Z, Song S, Khosla A, et al. 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE conference on