Page 28 - Read Online
P. 28
Page 126 Wu et al. Intell Robot 2022;2(2):10529 I http://dx.doi.org/10.20517/ir.2021.20
computer vision and pattern recognition; 2015. pp. 1912–20. DOI
32. JosephRivlin M, Zvirin A, Kimmel R. Momen (e) t: Flavor the moments in learning to classify shapes. In: Proceedings of the IEEE/CVF
International Conference on Computer Vision Workshops; 2019. pp. 0–0. DOI
33. Zhao H, Jiang L, Fu C, Jia J. PointWeb: Enhancing local neighborhood features for point cloud processing. In: 2019 IEEE/CVF
Conference on Computer Vision and Pattern Recognition; 2019. pp. 5560–68. DOI
34. Duan Y, Zheng Y, Lu J, Zhou J, Tian Q. Structural relational reasoning of point clouds. In: 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition; 2019. pp. 949–58. DOI
35. Yan X, Zheng C, Li Z, Wang S, Cui S. PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling.
In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. pp. 5588–97. DOI
36. Wu W, Qi Z, Fuxin L. PointConv: Deep convolutional networks on 3D point clouds. In: 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition; 2019. pp. 9613–22. DOI
37. Boulch A. Generalizing discrete convolutions for unstructured point clouds. In: Biasotti S, Lavoué G, Veltkamp R, editors. Eurographics
Workshop on 3D Object Retrieval. The Eurographics Association; 2019. pp. 71–78. DOI
38. Liu Y, Fan B, Xiang S, Pan C. Relationshape convolutional neural network for point cloud analysis. In: Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition; 2019. pp. 8895–904. DOI
39. Liu YC, Fan B, Meng G, et al. DensePoint: Learning densely contextual representation for efficient point cloud processing. 2019
IEEE/CVF International Conference on Computer Vision 2019:5238–47. DOI
40. Mao J, Wang X, Li H. Interpolated convolutional networks for 3D point cloud understanding. In: 2019 IEEE/CVF International
Conference on Computer Vision; 2019. pp. 1578–87. DOI
41. Rao Y, Lu J, Zhou J. Spherical fractal convolutional neural networks for point cloud recognition. 2019 IEEE/CVF Conference on
Computer Vision and Pattern Recognition 2019:452–60. DOI
42. Ye M, Xu S, Cao T, Chen Q. DRINet: A dualrepresentation iterative learning network for point cloud segmentation. In: Proceedings
of the IEEE/CVF International Conference on Computer Vision; 2021. pp. 7447–56. DOI
43. Deng Q, Li X, Ni P, Li H, Zheng Z. EnetCRFLidar: Lidar and camera fusion for multiscale object recognition. IEEE Access
2019;7:174335–44. DOI
44. Wang H, Lou X, Cai Y, Li Y, Chen L. Realtime vehicle detection algorithm based on vision and lidar point cloud fusion. J Sensors
2019;2019:8473980:1–:9. DOI
45. Qi CR, Liu W, Wu C, Su H, Guibas LJ. Frustum pointnets for 3D object detection from RGBD data. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern recognition; 2018. pp. 918–27. DOI
46. Lu H, Chen X, Zhang G, et al. SCANet: spatialchannel attention network for 3D object detection. In: ICASSP 20192019 IEEE
International Conference on Acoustics, Speech and Signal Processing. IEEE; 2019. pp. 1992–96. DOI
47. Liang M, Yang B, Chen Y, Hu R, Urtasun R. Multitask multisensor fusion for 3d object detection. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2019. pp. 7345–53. DOI
48. Qi CR, Chen X, Litany O, Guibas LJ. Imvotenet: boosting 3d object detection in point clouds with image votes. In: Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. pp. 4404–13. DOI
49. Qi CR, Litany O, He K, Guibas LJ. Deep hough voting for 3d object detection in point clouds. In: Proceedings of the IEEE International
Conference on Computer Vision; 2019. pp. 9277–86. DOI
50. Yang Z, Sun Y, Liu S, Shen X, Jia J. Ipod: Intensive pointbased object detector for point cloud. arXiv preprint arXiv:181205276 2018.
Available from: https://arxiv.org/abs/1812.05276.
51. Yang Z, Sun Y, Liu S, Shen X, Jia J. Std: Sparsetodense 3d object detector for point cloud. In: Proceedings of the IEEE International
Conference on Computer Vision; 2019. pp. 1951–60. DOI
52. Shi S, Wang X, Li H. Pointrcnn: 3d object proposal generation and detection from point cloud. In: Proceedings of the IEEE Conference
on Computer Vision and Pattern Recognition; 2019. pp. 770–79. DOI
53. Zarzar J, Giancola S, Ghanem B. PointRGCN: Graph convolution networks for 3D vehicles detection refinement. arXiv preprint
arXiv:191112236 2019. Available from: https://arxiv.org/abs/1911.12236.
54. Shi S, Wang Z, Shi J, Wang X, Li H. From points to parts: 3d object detection from point cloud with partaware and partaggregation
network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020. DOI
55. Ye M, Xu S, Cao T. Hvnet: Hybrid voxel network for lidar based 3d object detection. In: Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition; 2020. pp. 1631–40. DOI
56. Li Z, Wang F, Wang N. LiDAR RCNN: An efficient and universal 3D object detector. In: Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition; 2021. pp. 7546–55. DOI
57. Zhou Y, Tuzel O. Voxelnet: endtoend learning for point cloud based 3d object detection. In: Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition; 2018. pp. 4490–99. DOI
58. Lang AH, Vora S, Caesar H, et al. Pointpillars: fast encoders for object detection from point clouds. In: Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition; 2019. pp. 12697–705. DOI
59. He C, Zeng H, Huang J, Hua XS, Zhang L. Structure aware singlestage 3D object detection from point cloud. In: Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. pp. 11873–82. DOI
60. Dai J, Li Y, He K, Sun J. Rfcn: Object detection via regionbased fully convolutional networks. Advances in neural information
processing systems 2016;29. Available from: https://proceedings.neurips.cc/paper/2016/hash/577ef1154f3240ad5b9b413aa7346a1eAb
stract.html.