Abstract
Different from 3D models created by digital scanning devices, Structure From Motion (SfM) models are represented by point clouds with much sparser distributions. Noisy points in these representations are often unavoidable in practical applications, specifically when the accurate reconstruction of 3D surfaces is required, or when object registration and classification is performed in deep convolutional neural networks. Outliers and deformed geometric structures caused by computational errors in the SfM algorithms have a significant negative impact on the postprocessing of 3D point clouds in object and scene learning algorithms, indoor localization and automatic vehicle navigation, medical imaging, and many other applications. In this paper, we introduce several new methods to classify the points generated by the SfM process. We present a novel approach, Point-Cloud Optimization (PC-OPT), that integrates density-based filtering and surface smoothing for handling noisy points, and maintains the geometric integrity. Furthermore, an improved moving least squares (MLS) is constructed to smooth out the SfM geometry with varying scales.
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References
Berger, M., et al.: A survey of surface reconstruction from point clouds. In: Computer Graphics Forum, vol. 36, no. 1 (2017)
Schall, O., Belyaev, A., Seidel, H.-P.: Robust filtering of noisy scattered point data. In: Proceedings Eurographics/IEEE VGTC Symposium Point-Based Graphics. IEEE (2005)
Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: 2011 IEEE International Conference on Robotics and Automation. IEEE (2011)
Girardeau-Montaut, D.: CloudCompare (2016)
Steinke, F., Schölkopf, B., Blanz, V.: Support vector machines for 3D shape processing. In: Computer Graphics Forum, Amsterdam, vol. 24, no. 3. (1982, 2005)
Lancaster, P., Salkauskas, K.: Surfaces generated by moving least squares methods. Math. Comput. 37(155), 141–158 (1981)
Lee, I.-K.: Curve reconstruction from unorganized points. Comput. Aided Geom. Design 17(2), 161–177 (2000)
Levin, D.: Mesh-independent surface interpolation. In: Brunnett, G., Hamann, B., Müller, H., Linsen, L. (eds.) Geometric Modeling for Scientific Visualization. Mathematics and Visualization, pp. 37–49. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-662-07443-5_3
Alexa, M., et al.: Computing and rendering point set surfaces. IEEE Trans. Vis. Comput. Graph. 9(1), 3–15 (2003)
Pauly, M., Keiser, R., Kobbelt, L.P., Gross, M.: Shape modeling with point-sampled geometry. ACM Trans. Graph. (TOG) 22(3), 641–650 (2003)
Adamson, A., Alexa, M.: Anisotropic point set surfaces. In: Proceedings of the 4th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa (2006)
Guennebaud, G., Gross, M.: Algebraic point set surfaces. In: ACM SIGGRAPH 2007 papers, p. 23-es (2007)
Chen, J., et al.: Non-oriented MLS gradient fields. In: Computer Graphics Forum, vol. 32, no. 8 (2013)
Ohtake, Y., et al.: Multi-level partition of unity implicits. In: ACM SIGGRAPH 2005 Courses, p. 173-es (2005)
Lipman, Y., et al.: Parameterization-free projection for geometry reconstruction. ACM Trans. Graph. (TOG) 26(3), 22-es (2007)
Seitz, S.M., et al.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1. IEEE (2006)
Kolev, K., et al.: Continuous global optimization in multiview 3D reconstruction. Int. J. Comput. Vision 84(1), 80–96 (2009). https://doi.org/10.1007/s11263-009-0233-1
Sattler, T., Leibe, B., Kobbelt, L.: Improving image-based localization by active correspondence search. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 752–765. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33718-5_54
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Li, Y., Baciu, G. (2020). PC-OPT: A SfM Point Cloud Denoising Algorithm. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_25
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DOI: https://doi.org/10.1007/978-3-030-62362-3_25
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