GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud

YUE Yuanchen; CAI Yunfei; WANG Dongsheng

doi:10.1049/cje.2021.07.004

Volume 30 Issue 5

Sep. 2021

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YUE Yuanchen, CAI Yunfei, WANG Dongsheng, “GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 931-939, 2021, doi: 10.1049/cje.2021.07.004

Citation:

YUE Yuanchen, CAI Yunfei, WANG Dongsheng, “GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 931-939, 2021, doi: 10.1049/cje.2021.07.004

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GridNet-3D: A Novel Real-Time 3D Object Detection Algorithm Based on Point Cloud

doi: 10.1049/cje.2021.07.004

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Funds:

This work is supported by the Fund for Technical Field of the Foundation Strengthening Programme (No.2019-JCJQ-JJ-355).

Received Date: 2019-07-29
Available Online: 2021-09-02

Abstract

Abstract

3D object detection based on point cloud has an important application prospect in automatic driving technology. Aiming at the low precision of 3D object detection based on point cloud and the poor real-time performance caused by large numbers of 3D convolutions, a novel end-to-end real-time object detection algorithm named GridNet-3D is proposed. In the work, 2D gridmapping is used to preprocess the original point clouds. Then a novel structure grid encoding layer is adopted to encode point cloud features and is gotten grid feature maps in bird's eye view which is connected to region proposal network module to generate detections. Despite only using point clouds, the results on the KITTI 3D detection benchmark show that our algorithm has higher detection precision and better real-time performance on the detection of cars, pedestrians and cyclists, which has high practical value.
- Point cloud,
- Grid encoding layer,
- 3D object detection

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References(23)

References

Dong R, Jin L, Wu H., "Viewpoint optimization method for flying robot inspecting transmission towers based on point cloud model", Chinese Journal of Electronics, Vol.27, No.5, pp.976-984, 2018.

Qi C R, Su H, Mo K, et al., "Pointnet:Deep learning on point sets for 3d classification and segmentation", IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp.652-660, 2017.

Ren S, He K, Girshick R, et al., "Faster R-CNN:Towards real-time object detection with region proposal networks", Advances in Neural Information Processing Systems, Montreal, Quebec, Canada, pp.91-99, 2015.

Zhou Y and Tuzel O., "Voxelnet:End-to-end learning for point cloud based 3D object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, pp.4490-4499, 2018.

Geiger A, Lenz P and Urtasun R., "Are we ready for autonomous driving? The kitti vision benchmark suite", IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, pp.3354-3361, 2012.

Maturana D and Scherer S., "Voxnet:A 3D convolutional neural network for real-time object recognition", IEEE International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, pp.922-928, 2015.

Qi C R, Su H, Nießner M, et al., "Volumetric and multiview cnns for object classification on 3D data", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, pp.5648-5656, 2016.

Li Y, Pirk S, Su H, et al., "FPNN:Field probing neural networks for 3d data", Advances in Neural Information Processing Systems, Barcelona, Spain, pp.307-315, 2016.

Qi C R, Yi L, Su H, et al., "Pointnet++:Deep hierarchical feature learning on point sets in a metric space", Advances in Neural Information Processing Systems, Long Beach, CA, USA, pp.5099-5108, 2017.

Xu D, Anguelov D and Jain A., "Pointfusion:Deep sensor fusion for 3D bounding box estimation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Istanbul, Turkey, pp.244-253, 2018.

He K, Zhang X, Ren S, et al., "Deep residual learning for image recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, pp.770-778, 2016.

Qi C R, Liu W, Wu C, et al., "Frustum pointnets for 3D object detection from RGB-D data", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA, pp.918-927, 2018.

Yan Y, Mao Y and Li B., "Second:Sparsely embedded convolutional detection", Sensors, Vol.18, No.10, pp.33-37, 2018.

Ai Ali W, Abdelkarim S, Zahran M, et al., "YOLO3D:Endtoend real-time 3D oriented object bounding box detection from LiDAR point cloud", European Conference on Computer Vision, Munich, Germany, pp.442-450, 2018.

Beltran J, Guindel C, Moreno F M, et al., "BirdNet:A 3D object detection framework from LiDAR information", International Conference on Intelligent Transportation Systems (ITSC), Honolulu, Hawaii, USA, pp.3517-3523, 2018.

Liu W, Anguelov D, Erhan D, et al., "SSD:Single shot multibox detector", European Conference on Computer Vision, Amsterdam, Holland, pp.21-37, 2016.

Girshick R., "Fast R-CNN", Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, pp.1440-1448, 2015.

Lin T Y, Goyal P, Girshick R, et al., "Focal loss for dense object detection", Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, pp.2980-2988, 2017.

Li B, Zhang T and Xia T., "Vehicle detection from 3d lidar using fully convolutional network", https://arxiv.org/abs/1608.07916,2016-8-29.

Chen X, Ma H, Wan J, et al., "Multi-view 3D object detection network for autonomous driving", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA, pp.1907-1915, 2017.

M. Liang M, Yang B, Wang S, et al., "Deep continuous fusion for multi-sensor 3D object detection", Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, pp.641-656, 2018.

S Shin K, Kwon Y P and Tomizuka M., "Roarnet:A robust 3D object detection based on region approximation refinement", Intelligent Vehicles Symposium (IV), Paris, France, pp.2510-2515, 2019.

Wang D Z and Posner I., "Voting for voting in online point cloud object detection", Robotics:Science and Systems, pp.1156-1165, 2015.

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