Detecting people in sprinting motion using HPRDenoise: Point cloud denoising with hidden point removal
Abstract
LiDARs are utilized in various applications, such as self-driving vehicles and robotics, to aid in sensing the environment. However, LiDARs do not provide instantaneous images and they generate noise, adding to measurement errors. This noise, often referred to as motion blur phenomenon also observed in other imaging sensors results in decreased sensing accuracy for moving objects. This study introduces HPRDenoise, a noise reduction method based on hidden point removal, specifically designed to reduce motion blur during sprinting motion. This method capitalizes on the occlusion produced by a fixed-position LiDAR. We propose a comprehensive denoising approach to filter points from a point cloud without resorting to supervised learning, unlike most existing denoising algorithms. The number of correct frames and accuracy were compared for Raw, ScoreDenoise, which is the state-of-the-art method for random point cloud denoising, and HPRDenoise (Ours). Accuracy is defined as the ratio of the number of correct frames to the total number of frames. Experimental results demonstrate that the detection accuracy of point clouds processed with HPRDenoise is 72.73%, achieving better accuracy than those using conventional methods.
Keywords
Full Text:
PDFReferences
1. Pueo B, Jimenez-Olmedo JM. Application of motion capture technology for sport performance analysis. Retos. 2017; (32): 241-247. doi: 10.47197/retos.v0i32.56072
2. Rekant J, Rothenberger S, Chambers A. Inertial measurement unit-based motion capture to replace camera-based systems for assessing gait in healthy young adults: Proceed with caution. Measurement: Sensors. 2022; 23: 100396. doi: 10.1016/j.measen.2022.100396
3. Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition. Published online June 2012. doi: 10.1109/cvpr.2012.6248074
4. Caesar H, Bankiti V, Lang AH, et al. nuScenes: A Multimodal Dataset for Autonomous Driving. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2020. doi: 10.1109/cvpr42600.2020.01164
5. Sun P, Kretzschmar H, Dotiwalla X, et al. Scalability in Perception for Autonomous Driving: Waymo Open Dataset. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2020. doi: 10.1109/cvpr42600.2020.00252
6. Yang W, Gong Z, Huang B, et al. Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera. IEEE Robotics and Automation Letters. 2022; 7(3): 8241-8248. doi: 10.1109/lra.2022.3187506
7. Yin T, Zhou X, Krahenbuhl P. Center-based 3D Object Detection and Tracking. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2021. doi: 10.1109/cvpr46437.2021.01161
8. Lang AH, Vora S, Caesar H, et al. PointPillars: Fast Encoders for Object Detection from Point Clouds. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2019. doi: 10.1109/cvpr.2019.01298
9. Shi S, Guo C, Jiang L, et al. PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2020. doi: 10.1109/cvpr42600.2020.01054
10. Shi W, Rajkumar R. Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Published online June 2020. doi: 10.1109/cvpr42600.2020.00178
11. Charles RQ, Su H, Kaichun M, et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Published online July 2017. doi: 10.1109/cvpr.2017.16
12. Yin S, Li H, Laghari AA, et al. An Anomaly Detection Model Based on Deep Auto-Encoder and Capsule Graph Convolution via Sparrow Search Algorithm in 6G Internet-of-Everything. IEEE Internet of Things Journal. Published online 2024: 1-1. doi: 10.1109/jiot.2024.3353337
13. Zhao Z, Zheng P, Xu S, Wu X. Object Detection Based on Deep Learning: A Brief Review. IJLAI Transactions on Science and Engineering. 2023; 1(2): 1-6.
14. Jiang M, Yin S. Facial expression recognition based on convolutional block attention module and multi-feature fusion. International Journal of Computational Vision and Robotics. 2023; 13(1): 21. doi: 10.1504/ijcvr.2023.127298
15. Luo S, Hu W. Score-Based Point Cloud Denoising. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). Published online October 2021. doi: 10.1109/iccv48922.2021.00454
16. Huang H, Wu S, Gong M, et al. Edge-aware point set resampling. ACM Transactions on Graphics. 2013; 32(1): 1-12. doi: 10.1145/2421636.2421645
17. Mattei E, Castrodad A. Point Cloud Denoising via Moving RPCA. Computer Graphics Forum. 2016; 36(8): 123-137. doi: 10.1111/cgf.13068
18. Sun Y, Schaefer S, Wang W. Denoising point sets via L0 minimization. Computer Aided Geometric Design. 2015; 35-36: 2-15. doi: 10.1016/j.cagd.2015.03.011
19. Faisal Zaman and Ya Ping Wong and Boon Yian Ng: Density-based Denoising of Point Cloud, CoRR, http://arxiv.org/abs/1602.05312 (2021)
20. Qi, CR, Yi L, et al. PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017. pp. 5105-5114.
21. Wang Y, Sun Y, Liu Z, et al. Dynamic Graph CNN for Learning on Point Clouds. ACM Transactions on Graphics. 2019; 38(5): 1-12. doi: 10.1145/3326362
22. Duan C, Chen S, Kovacevic J. 3D Point Cloud Denoising via Deep Neural Network Based Local Surface Estimation. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Published online May 2019. doi: 10.1109/icassp.2019.8682812
23. Rakotosaona M, La Barbera V, Guerrero P, et al. PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds. Computer Graphics Forum. 2019; 39(1): 185-203. doi: 10.1111/cgf.13753
24. Hermosilla P, Ritschel T, Ropinski T. Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2019.
25. Pistilli F, Fracastoro G, Valsesia D, Magli E. Learning Graph-Convolutional Representationsfor Point Cloud Denoising, The European Conference on Computer Vision (ECCV). 2020. pp. 103-118.
26. Lin J, Zhang F. Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV. Published online 2019. doi: 10.48550/ARXIV.1909.06700
27. Katz S, Tal A, Basri R. Direct visibility of point sets. ACM Transactions on Graphics. 2007; 26(3): 24. doi: 10.1145/1276377.1276407
28. Fischler MA, Bolles RC. Random sample consensus. Communications of the ACM. 1981; 24(6): 381-395. doi: 10.1145/358669.358692
29. Mariga L. pyRANSAC-3D. Available online: https://github.com/leomariga/pyRANSAC-3D (accessed on 2 March 2023).
30. Campello RJGB, Moulavi D, Zimek A, et al. Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data. 2015; 10(1): 1-51. doi: 10.1145/2733381
31. LIVOX: livox ROS driver, 2021. Available online: https://github.com/libov-SDK/livox_ros_driver (accessed on 2 March 2023).
DOI: https://doi.org/10.32629/jai.v7i5.1634
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Taku Itami, Yuki Takeyama, Sota Akamine, Jun Yoneyema, Sebastien Ibarboure
License URL: https://creativecommons.org/licenses/by-nc/4.0/