Abstract
Recovering the shape of an object from a 3D point cloud is a challenging task due to the complex shapes and diverse requirements of different tasks. For buildings in urban scenes, maintaining geometric constraints such as parallelism, perpendicularity, symmetry, and coplanarity is important during the model fitting process. In this paper, we present a regularity-constrained point cloud reconstruction framework that comprises primitive initialization, constraint construction, and global optimization. Our approach first performs normal optimization and plane segmentation on the input point cloud. We then compute the global reference directions to set the target normal for each plane to construct the constraints. Finally, we obtain the model by globally optimizing the position and orientation of the planes while considering the constraints. Our experimental results demonstrate that our proposed algorithm not only strictly enforces geometric constraints but also closely fits the input point cloud. Furthermore, our framework outperforms state-of-the-art methods in terms of shape recovery and constraint maintenance, as demonstrated by comparative evaluations.
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References
Bauchet, J.-P., Lafarge, F.: Kinetic shape reconstruction. ACM Trans. Graph. (TOG) 39(5), 1–14 (2020)
Chen, Z., Ledoux, H., Khademi, S., Nan, L.: Reconstructing compact building models from point clouds using deep implicit fields. ISPRS J. Photogramm. Remote. Sens. 194, 58–73 (2022)
Dalitz, C., Schramke, T., Jeltsch, M.: Iterative Hough transform for line detection in 3D point clouds. Image Process. On Line 7, 184–196 (2017)
Derpanis, K.G.: Overview of the RANSAC algorithm. Image Rochester NY 4(1), 2–3 (2010)
Erler, P., Guerrero, P., Ohrhallinger, S., Mitra, N.J., Wimmer, M.: Points2surf learning implicit surfaces from point clouds. In: European Conference on Computer Vision, pp. 108–124. Springer (2020)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Hazewinkel, M.: Orthogonalization. In: Encyclopedia of Mathematics. Kluwer Academic Publishers, New York (2001)
Huang, L.L., Li, W.G., Yang, Q.L., Chen, Y.C.: Segmentation algorithm of three-dimensional point cloud data based on region growing. In: Applied Mechanics and Materials
Hulik, R., Spanel, M., Smrz, P., Materna, Z.: Continuous plane detection in point-cloud data based on 3D Hough transform. J. Vis. Commun. Image Represent. 25(1), 86–97 (2014)
Isack, H., Boykov, Y.: Energy-based geometric multi-model fitting. Int. J. Comput. Vis. 97(2), 123–147 (2012)
Jenke, P., Wand, M., Bokeloh, M., Schilling, A., Straßer, W.: Bayesian point cloud reconstruction. In: Computer Graphics Forum, vol. 25, pp. 379–388. Wiley (2006)
Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proceedings of the Fourth Eurographics Symposium on Geometry Processing, vol. 7 (2006)
Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. (ToG) 32(3), 1–13 (2013)
Kelly, T., Femiani, J., Wonka, P., Mitra, N.J.: Bigsur: large-scale structured urban reconstruction. ACM Trans. Graph. 36(6) (2017)
Khaloo, A., Lattanzi, D.: Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models. Adv. Eng. Inform. 34, 1–16 (2017)
Korman, S., Litman, R.: Latent RANSAC. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6693–6702 (2018)
Li, L., Sung, M., Dubrovina, A., Yi, L., Guibas, L.J.: Supervised fitting of geometric primitives to 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2652–2660 (2019)
Li, L., Yang, F., Zhu, H., Li, D., Li, Y., Tang, L.: An improved RANSAC for 3D point cloud plane segmentation based on normal distribution transformation cells. Remote Sens. 9(5), 433 (2017)
Li, Y., Wu, X., Chrysanthou, Y., Sharf, A., Cohen-Or, D., Mitra, N.J.: GlobFit: consistently fitting primitives by discovering global relations. ACM Trans. Graph. 30(4), 52:1-52:12 (2011)
Lim, S.P., Haron, H.: Surface reconstruction techniques: a review. Artif. Intell. Rev. 42(1), 59–78 (2014)
Lin, Y., Li, J., Wang, C., Chen, Z., Wang, Z., Li, J.: Fast regularity-constrained plane fitting. ISPRS J. Photogramm. Remote. Sens. 161, 208–217 (2020)
Madsen, K., Nielsen, H.B., Tingleff, O.: Methods for non-linear least squares problems (2004)
Mandikal, P., Radhakrishnan, V.B.: Dense 3d point cloud reconstruction using a deep pyramid network. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1052–1060. IEEE (2019)
Monszpart, A., Mellado, N., Brostow, G.J., Mitra, N.J.: RAPter: rebuilding man-made scenes with regular arrangements of planes. ACM Trans. Graph. 34(4), 103:1-103:12 (2015)
Musialski, P., Wonka, P., Aliaga, D.G., Wimmer, M., Van Gool, L., Purgathofer, W.: A survey of urban reconstruction. In: Computer Graphics Forum, vol. 32, pp. 146–177. Wiley Online Library (2013)
Nan, L., Sharf, A., Zhang, H., Cohen-Or, D., Chen, B.: Smartboxes for interactive urban reconstruction. In: ACM SIGGRAPH 2010 papers, pp. 1–10 (2010)
Nan, L., Wonka, P.: Polyfit: polygonal surface reconstruction from point clouds. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2353–2361 (2017)
Oesau, S., Lafarge, F., Alliez, P.: Planar shape detection and regularization in tandem. In: Computer Graphics Forum, vol. 35, pp. 203–215. Wiley Online Library (2016)
Schnabel, R., Wahl, R., Klein, R.: Efficient RANSAC for point-cloud shape detection. In: Computer Graphics Forum, vol. 26, pp. 214–226. Wiley Online Library (2007)
Sharma, G., Liu, D., Maji, S., Kalogerakis, E., Chaudhuri, S., Měch, R.: Parsenet: a parametric surface fitting network for 3d point clouds. In: European Conference on Computer Vision, pp. 261–276. Springer (2020)
Sheung, H., Wang, C.C.: Robust mesh reconstruction from unoriented noisy points. In: 2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling, pp. 13–24 (2009)
Vo, A.-V., Truong-Hong, L., Laefer, D.F., Bertolotto, M.: Octree-based region growing for point cloud segmentation. ISPRS J. Photogramm. Remote. Sens. 104, 88–100 (2015)
Wang, S., Cai, G., Cheng, M., Junior, J.M., Huang, S., Wang, Z., Su, S., Li, J.: Robust 3D reconstruction of building surfaces from point clouds based on structural and closed constraints. ISPRS J. Photogramm. Remote. Sens. 170, 29–44 (2020)
Xu, R., Wang, Z., Dou, Z., Zong, C., Xin, S., Jiang, M., Ju, T., Tu, C.: Rfeps: reconstructing feature-line equipped polygonal surface. ACM Trans. Graph. (TOG) 41(6), 1–15 (2022)
Yan, S., Yang, Z., Ma, C., Huang, H., Vouga, E., Huang, Q.: Hpnet: deep primitive segmentation using hybrid representations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2753–2762 (2021)
Yang, M.Y., Förstner, W.: Plane detection in point cloud data. In: Proceedings of the 2nd International Conference on Machine Control Guidance, Bonn, vol. 1, pp. 95–104 (2010)
Zhang, L., Liu, L., Gotsman, C., Huang, H.: Mesh reconstruction by meshless denoising and parameterization. Comput. Graph. 34(3), 198–208 (2010)
Acknowledgements
The research was supported by National Natural Science Foundation of China (61972327, 62272402), Special Fund for Key Program of Science and Technology of Fujian Province (2022YZ040011), Natural Science Foundation of Fujian Province (2022J01001), and Fundamental Research Funds for the Central Universities (20720220037).
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Yu, H., Cao, J., Liu, X. et al. Regularity-constrained point cloud reconstruction of building models via global alignment. Vis Comput 40, 8363–8375 (2024). https://doi.org/10.1007/s00371-023-03241-x
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DOI: https://doi.org/10.1007/s00371-023-03241-x