Abstract
In order to extract the spatial structure features of the original point cloud, multi layers pointpillars model, a fast and efficient one-stage network, is proposed for object detection from point cloud. Firstly, point cloud are divided into multi layers along z axis, by each layer to generate pillars in the vertical direction, and multi layers pseudo-image representing for multi layers are created by the method of pointpillars. Then, the multi layers and complete pseudo-image are fused as the input of RPN, and the feature maps with context information and multi-scale features are obtained. Finally, the detection boxes and classification score were obtained by SSD head according to the feature maps. We get a high quality prediction box and classification results. The experimental results show that multi-layer pointpillars can get higher precision than the original pointpillars.
The first author is a student. The work is supported by the National Natural Science Foundation of China (No. 41971424, No. 61701191), Xiamen Science and Technology Project (No. 3502Z20183032, No. 3502Z20191022, No. 3502Z20203057).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Qi, C.R., et al.: Frustum PointNets for 3D Object Detection from RGB-D Data (2017)
Ku, J., et al.: Joint 3D Proposal Generation and Object Detection from View Aggregation (2017)
Yang, B., Luo, W., Urtasun, R.: PIXOR: Real-time 3D Object Detection from Point Clouds (2018)
Liang, M., Yang, B., Wang, S., Urtasun, R.: Deep continuous fusion for multi-sensor 3D object detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 663–678. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_39
Qi, C.R., et al.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation (2016)
Qi, C.R., et al.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017)
Shi, S., Wang, X., Li, H.: PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud (2018)
Shi, S., et al.: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network (2019)
Lang, A.H., et al.: PointPillars: Fast Encoders for Object Detection from Point Clouds (2018)
Riegler, G., Ulusoy, A.O., Geiger, A.: OctNet: Learning Deep 3D Representations at High Resolutions (2016)
Song, S., et al.: Semantic scene completion from a single depth image. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Zhou, Y., Tuzel, O.: VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection (2017)
Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)
Wu, B., et al.: SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud (2017)
Liu, W., et al.: SSD: Single Shot MultiBox Detector (2016)
Chen, X., et al.: Multi-View 3D Object Detection Network for Autonomous Driving (2016)
Yang, Z., et al.: STD: Sparse-to-Dense 3D Object Detector for Point Cloud (2019)
Engelcke, M., et al.: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks (2016)
Shin, K., Kwon, Y.P., Tomizuka, M.: RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement (2018)
Simon, M., et al.: Complex-YOLO: Real-time 3D Object Detection on Point Clouds (2018)
Chen, L.C., et al.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834 (2018)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2012)
Song, S., Xiao, J.: Deep sliding shapes for amodal 3D object detection in RGB-D images. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2016)
Xu, D., Anguelov, D., Jain, A.: PointFusion: deep sensor fusion for 3D bounding box estimation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2018)
Zhao, H., et al.: Pyramid scene parsing network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Gonzalez, A., et al.: Multiview random forest of local experts combining RGB and LIDAR data for pedestrian detection. In: 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE (2015)
Jiang, M., et al.: PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation (2018)
Li, Y., et al.: PointCNN: Convolution on \(\cal{X}\)-Transformed Points (2018)
Li, J., Chen, B.M., Lee, G.H.: SO-Net: Self-Organizing Network for Point Cloud Analysis (2018)
Dai, A.: ScanNet: richly-annotated 3D reconstructions of indoor scenes. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, S. et al. (2020). Multi-layer Pointpillars: Multi-layer Feature Abstraction for Object Detection from Point Cloud. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-60633-6_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60632-9
Online ISBN: 978-3-030-60633-6
eBook Packages: Computer ScienceComputer Science (R0)