点云目标检测残差投票网络

doi:10.12052/gdutxb.200176

Abstract

Abstract: High-precision 3D object detection is a key technology to realize object perception, which is of great significance to the implementation of applications such as automatic driving and robot control. In order to improve the accuracy of 3D object detection, the algorithm VoteNet is improved, and an end-to-end high-precision 3D point cloud target detection network based on residual network, ResVoteNet is proposed. Specifically, a residual network skeleton suitable for point cloud data is designed, and a residual feature extraction module and a residual up-sampling module are proposed and integrated into the VoteNet framework. The introduction of the residual network structure enhances the network's feature extraction and learning capabilities for point cloud data, and improves the robustness of the model. The algorithm is tested on the publicly available large-scale point cloud data sets SCANNET and SUN-RGBD, and the average detection accuracy mAP has reached 61.1% and 59.9%, respectively, surpassing other current state-of-the-art algorithms.

Key words: 3D point cloud, object detection, residual network

CLC Number:

TP391.4

Yang Ji-sheng, Zhang Yun, Li Dong. A Residual Neural Network with Voting for 3D Object Detection in Point Clouds[J].Journal of Guangdong University of Technology, 2022, 39(01): 56-62.

References

[1] 王玺. 谷歌公布无人驾驶车事故细节未来交通尚仍是梦[J]. 华东科技, 2016, 4(1): 10-15.
[2] YAN Y, MAO Y, LI B. Second: Sparsely embedded convolutional detection [J]. Sensors, 2018, 18(10): 3337.
[3] LANG A H, VORA S, CAESAR H, et al. Pointpillars: fast encoders for object detection from point clouds[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 12697-12705.
[4] 杨泽鑫, 彭林才, 刘定宁, 等. 基于PCL和Qt的点云处理系统设计与开发[J]. 广东工业大学学报, 2017, 34(6): 61-67.
YANG Z X, PENG L C, LIU D N, et al. Development of point cloud processing system based on PCL and Qt [J]. Journal of Guangdong University of Technology, 2017, 34(6): 61-67.
[5] ZHOU Y, TUZEL O. Voxelnet: end-to-end learning for point cloud based 3D object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 4490-4499.
[6] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 779-788.
[7] QI C R, LIU W, WU C, et al. Frustum pointnets for 3D object detection from RGB-D data[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 918-927.
[8] QI C R, SU H, MO K, et al. Pointnet: deep learning on point sets for 3D classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI: IEEE, 2017: 652-660.
[9] QI C R, LITANY O, HE K, et al. Deep hough voting for 3D object detection in point clouds[C]//Proceedings of the IEEE International Conference on Computer Vision. Long Beach: IEEE, 2019: 9277-9286.
[10] SHI S, GUO C, JIANG L, et al. PV-RCNN: point-voxel feature set abstraction for 3D object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 10529-10538.
[11] LIU Z, ZHAO X, HUANG T, et al. TANet: robust 3D object detection from point clouds with triple attention[C]//Proceedings of the AAAI Conference on Artificial Intelligence. New York: AAAI, 2020: 11677-11684.
[12] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[13] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3431-3440.
[14] DAI A, CHANG A X, SAVVA M, et al. Scannet: richly-annotated 3D reconstructions of indoor scenes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 5828-5839.
[15] SONG S, LICHTENBERG S P, XIAO J. Sun RGB-D: a RGB-D scene understanding benchmark suite[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 567-576.
[16] SONG S, XIAO J. Deep sliding shapes for a modal 3D object detection in RGB-D images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 808-816.
[17] HOU J, DAI A, NIEBNER M. 3D-SIS: 3D semantic instance segmentation of RGB-D scans[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 4421-4430.
[18] LAHOUD J, GHANEM B. 2D-driven 3D object detection in RGB-D images[C]//Proceedings of the IEEE International Conference on Computer Vision. Honolulu: IEEE, 2017: 4622-4630.
[19] REN Z, SUDDERTH E B. Three-dimensional object detection and layout prediction using clouds of oriented gradients[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1525-1533.
[20] HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. Honolulu: IEEE, 2017: 2961-2969.
[21] YI L, ZHAO W, WANG H, et al. GSPN: generative shape proposal network for 3D instance segmentation in point cloud[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3947-3956.
[22] QI C R, YI L, SU H, et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space [J]. Advances in Neural Information Processing Systems, 2017, 30: 5099-5108.

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