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3D Point Cloud Outliers and Noise Reduction Using Neural Networks

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Telematics and Computing (WITCOM 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1906))

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Abstract

3D point clouds find widespread use in various areas of computing research, such as 3D reconstruction, point cloud segmentation, navigation, and assisted driving, to name a few examples. A point cloud is a collection of coordinates that represent the shape or surface of an object or scene. One way to generate these point clouds is by using RGB-D cameras. However, one major issue when using point clouds is the presence of noise and outliers caused by various factors, such as environmental conditions, object reflectivity, and sensor limitations. Classification and segmentation tasks can become complex when point clouds contain noise and outliers. This paper proposes a method to reduce outliers and noise in 3D point clouds. Our proposal builds on a deep learning architecture called PointCleanNet, which we modified by adding extra convolutional layers to extract feature maps that help classify point cloud outliers. We demonstrate the effectiveness of our proposed method in improving outlier classification and noise reduction in non-dense point clouds. We achieved this by including a low-density point cloud dataset in the training stage, which helped our method classify outliers more efficiently than PointCleanNet and Luo, S, et al.

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References

  1. Antonopoulos, A., Lagoudakis, M.G., Partsinevelos, P.: A ROS multi-tier UAV localization module based on GNSS, inertial and visual-depth data. Drones 6(6), 135 (2022)

    Article  Google Scholar 

  2. Chidsin, W., Gu, Y., Goncharenko, I.: AR-based navigation using RGB-D camera and hybrid map. Sustainability 13(10), 5585 (2021)

    Article  Google Scholar 

  3. Costantino, D., Vozza, G., Pepe, M., Alfio, V.S.: Smartphone lidar technologies for surveying and reality modelling in urban scenarios: evaluation methods, performance and challenges. Appl. Syst. Innov. 5(4), 63 (2022)

    Article  Google Scholar 

  4. Debeunne, C., Vivet, D.: A review of visual-lidar fusion based simultaneous localization and mapping. Sensors 20(7), 2068 (2020)

    Article  Google Scholar 

  5. Edelmers, E., Kazoka, D., Pilmane, M.: Creation of anatomically correct and optimized for 3D printing human bones models. Appl. Syst. Innov. 4(3), 67 (2021)

    Article  Google Scholar 

  6. Fu, Y., Yan, Q., Yang, L., Liao, J., Xiao, C.: Texture mapping for 3D reconstruction with RGB-D sensor. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4645–4653 (2018)

    Google Scholar 

  7. Giancola, S., Valenti, M., Sala, R.: A Survey on 3D Cameras: Metrological Comparison of Time-of-Flight, Structured-Light and Active Stereoscopy Technologies. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91761-0

    Book  Google Scholar 

  8. Guerrero, P., Kleiman, Y., Ovsjanikov, M., Mitra, N.J.: PCPNet learning local shape properties from raw point clouds. In: Computer Graphics Forum, vol. 37, pp. 75–85. Wiley Online Library (2018)

    Google Scholar 

  9. Herban, S., Costantino, D., Alfio, V.S., Pepe, M.: Use of low-cost spherical cameras for the digitisation of cultural heritage structures into 3D point clouds. J. Imaging 8(1), 13 (2022)

    Article  Google Scholar 

  10. Horio, M., et al.: Resolving multi-path interference in compressive time-of-flight depth imaging with a multi-tap macro-pixel computational CMOS image sensor. Sensors 22(7), 2442 (2022)

    Article  Google Scholar 

  11. Kim, S., Moon, H., Oh, J., Lee, Y., Kwon, H., Kim, S.: Automatic measurements of garment sizes using computer vision deep learning models and point cloud data. Appl. Sci. 12(10), 5286 (2022)

    Article  Google Scholar 

  12. Klingensmith, M., Dryanovski, I., Srinivasa, S.S., Xiao, J.: CHISEL: real time large scale 3D reconstruction onboard a mobile device using spatially hashed signed distance fields. In: Robotics: Science and Systems, vol. 4. Citeseer (2015)

    Google Scholar 

  13. Ko, K., Gwak, H., Thoummala, N., Kwon, H., Kim, S.H.: SqueezeFace: integrative face recognition methods with lidar sensors. J. Sens. 2021 (2021)

    Google Scholar 

  14. Li, J., Gao, W., Wu, Y., Liu, Y., Shen, Y.: High-quality indoor scene 3d reconstruction with RGB-D cameras: a brief review. Comput. Vis. Media 1–25 (2022)

    Google Scholar 

  15. Liu, Z., Zhao, C., Wu, X., Chen, W.: An effective 3D shape descriptor for object recognition with RGB-D sensors. Sensors 17(3), 451 (2017)

    Article  Google Scholar 

  16. Long, N., Yan, H., Wang, L., Li, H., Yang, Q.: Unifying obstacle detection, recognition, and fusion based on the polarization color stereo camera and lidar for the ADAS. Sensors 22(7), 2453 (2022)

    Article  Google Scholar 

  17. Luo, S., Hu, W.: Score-based point cloud denoising. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4583–4592 (2021)

    Google Scholar 

  18. Morell-Gimenez, V., et al.: A comparative study of registration methods for RGB-D video of static scenes. Sensors 14(5), 8547–8576 (2014)

    Article  Google Scholar 

  19. Na, M.H., Cho, W.H., Kim, S.K., Na, I.S.: Automatic weight prediction system for Korean cattle using Bayesian ridge algorithm on RGB-D image. Electronics 11(10), 1663 (2022)

    Article  Google Scholar 

  20. Ning, X., Li, F., Tian, G., Wang, Y.: An efficient outlier removal method for scattered point cloud data. PLoS ONE 13(8), e0201280 (2018)

    Article  Google Scholar 

  21. Oliveira, M., Santos, V., Sappa, A.D., Dias, P., Moreira, A.P.: Incremental texture mapping for autonomous driving. Robot. Auton. Syst. 84, 113–128 (2016)

    Article  Google Scholar 

  22. Pan, Y., Chen, C., Li, D., Zhao, Z., Hong, J.: Augmented reality-based robot teleoperation system using RGB-D imaging and attitude teaching device. Robot. Comput.-Integr. Manuf. 71, 102167 (2021)

    Article  Google Scholar 

  23. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)

    Google Scholar 

  24. Rakotosaona, M.-J., La Barbera, V., Guerrero, P., Mitra, N.J., Ovsjanikov, M.: PointCleanNet: learning to denoise and remove outliers from dense point clouds. In: Computer Graphics Forum, vol. 39, pp. 185–203. Wiley Online Library (2020)

    Google Scholar 

  25. Royo, S., Ballesta-Garcia, M.: An overview of lidar imaging systems for autonomous vehicles. Appl. Sci. 9(19), 4093 (2019)

    Article  Google Scholar 

  26. Schneider, P., et al.: Timo-a dataset for indoor building monitoring with a time-of-flight camera. Sensors 22(11), 3992 (2022)

    Article  Google Scholar 

  27. Song, Y., Xu, F., Yao, Q., Liu, J., Yang, S.: Navigation algorithm based on semantic segmentation in wheat fields using an RGB-D camera. Inf. Process. Agric. (2022)

    Google Scholar 

  28. Sotoodeh, S.: Outlier detection in laser scanner point clouds. Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci. 36(5), 297–302 (2006)

    Google Scholar 

  29. Sui, W., Wang, L., Fan, B., Xiao, H., Huaiyu, W., Pan, C.: Layer-wise floorplan extraction for automatic urban building reconstruction. IEEE Trans. Visual Comput. Graphics 22(3), 1261–1277 (2015)

    Article  Google Scholar 

  30. Sun, Y., Luo, Y., Zhang, Q., Xu, L., Wang, L., Zhang, P.: Estimation of crop height distribution for mature rice based on a moving surface and 3d point cloud elevation. Agronomy 12(4), 836 (2022)

    Article  Google Scholar 

  31. Tagarakis, A.C., Kalaitzidis, D., Filippou, E., Benos, L., Bochtis, D.: 3D scenery construction of agricultural environments for robotics awareness. In: Bochtis, D.D., Sørensen, C.G., Fountas, S., Moysiadis, V., Pardalos, P.M. (eds.) Information and Communication Technologies for Agriculture—Theme III: Decision. Springer Optimization and Its Applications, vol. 184, pp. 125–142. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-84152-2_6

  32. Tan, F., Xia, Z., Ma, Y., Feng, X.: 3D sensor based pedestrian detection by integrating improved HHA encoding and two-branch feature fusion. Remote Sens. 14(3), 645 (2022)

    Article  Google Scholar 

  33. Tanzer, M., Laverdière, C., Barimani, B., Hart, A.: Augmented reality in arthroplasty: an overview of clinical applications, benefits, and limitations. J. Am. Acad. Orthop. Surg. 30(10), e760–e768 (2022)

    Article  Google Scholar 

  34. Trujillo-Jiménez, M.A., et al.: body2vec: 3D point cloud reconstruction for precise anthropometry with handheld devices. J. Imaging 6(9), 94 (2020)

    Article  Google Scholar 

  35. Visa, S., Ramsay, B., Ralescu, A.L., Van Der Knaap, E.: Confusion matrix-based feature selection. In: MAICS, vol. 710, pp. 120–127 (2011)

    Google Scholar 

  36. Vogt, M., Rips, A., Emmelmann, C.: Comparison of ipad pro®’s lidar and truedepth capabilities with an industrial 3d scanning solution. Technologies 9(2), 25 (2021)

    Article  Google Scholar 

  37. Wang, F., et al.: Object-based reliable visual navigation for mobile robot. Sensors 22(6), 2387 (2022)

    Article  Google Scholar 

  38. Weinmann, M., et al.: Reconstruction and Analysis of 3D Scenes. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29246-5

    Book  Google Scholar 

  39. Wu, T., Pan, L., Zhang, J., Wang, T., Liu, Z., Lin, D.: Density-aware chamfer distance as a comprehensive metric for point cloud completion. arXiv preprint arXiv:2111.12702 (2021)

  40. Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)

    Article  Google Scholar 

  41. Yu, K., Eck, U., Pankratz, F., Lazarovici, M., Wilhelm, D., Navab, N.: Duplicated reality for co-located augmented reality collaboration. IEEE Trans. Visual Comput. Graphics 28(5), 2190–2200 (2022)

    Article  Google Scholar 

  42. Yuan, Z., Li, Y., Tang, S., Li, M., Guo, R., Wang, W.: A survey on indoor 3D modeling and applications via RGB-D devices. Front. Inf. Technol. Electron. Eng. 22(6), 815–826 (2021)

    Article  Google Scholar 

  43. Zhang, G., Geng, X., Lin, Y.-J.: Comprehensive mPoint: a method for 3D point cloud generation of human bodies utilizing FMCW MIMO mm-wave radar. Sensors 21(19), 6455 (2021)

    Article  Google Scholar 

  44. Zheng, H., Wang, W., Wen, F., Liu, P.: A complementary fusion strategy for RGB-D face recognition. In: Þór Jónsson, B., et al. (eds.) MMM 2022. LNCS, vol. 13141, pp. 339–351. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-98358-1_27

  45. Zollhöfer, M., et al.: State of the art on 3D reconstruction with RGB-D cameras. In: Computer graphics forum, vol. 37, pp. 625–652. Wiley Online Library (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, 76010, Santiago de Querétaro, Mexico

    Luis-Rogelio Roman-Rivera, Jesus Carlos Pedraza-Ortega, Israel Sotelo-Rodríguez & Manuel Toledano-Ayala

  2. Cuerpo Académico de Ingeniería de Biosistemas, Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, 76010, Santiago de Querétaro, Mexico

    Ramón Gerardo Guevara-González

Authors
  1. Luis-Rogelio Roman-Rivera
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  2. Jesus Carlos Pedraza-Ortega
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  3. Israel Sotelo-Rodríguez
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  4. Ramón Gerardo Guevara-González
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  5. Manuel Toledano-Ayala
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Correspondence to Luis-Rogelio Roman-Rivera .

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Editors and Affiliations

  1. UPIITA - Instituto Politécnico Nacional, México, Mexico

    Miguel Félix Mata-Rivera

  2. ESCOM - Instituto Politécnico Nacional, México, Mexico

    Roberto Zagal-Flores

  3. Universidad Mayor, Santiago, Chile

    Cristian Barria-Huidobro

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Roman-Rivera, LR., Pedraza-Ortega, J.C., Sotelo-Rodríguez, I., Guevara-González, R.G., Toledano-Ayala, M. (2023). 3D Point Cloud Outliers and Noise Reduction Using Neural Networks. In: Mata-Rivera, M.F., Zagal-Flores, R., Barria-Huidobro, C. (eds) Telematics and Computing. WITCOM 2023. Communications in Computer and Information Science, vol 1906. Springer, Cham. https://doi.org/10.1007/978-3-031-45316-8_21

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  • DOI: https://doi.org/10.1007/978-3-031-45316-8_21

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