Abstract
The captured 3D point clouds by depth cameras and 3D scanners are often corrupted by noise, so point cloud denoising is typically required for downstream applications. We observe that: (i) the scale of the local neighborhood has a significant effect on the denoising performance against different noise levels, point intensities, as well as various kinds of local details; (ii) non-iteratively evolving a noisy input to its noise-free version is non-trivial; (iii) both traditional geometric methods and learning-based methods often lose geometric features with denoising iterations, and (iv) most objects can be regarded as piece-wise smooth surfaces with a small number of features. Motivated by these observations, we propose a novel and task-specific point cloud denoising network, named RePCD-Net, which consists of four key modules: (i) a recurrent network architecture to effectively remove noise; (ii) an RNN-based multi-scale feature aggregation module to extract adaptive features in different denoising stage; (iii) a recurrent propagation layer to enhance the geometric feature perception across stages; and (iv) a feature-aware CD loss to regularize the predictions towards multi-scale geometric details. Extensive qualitative and quantitative evaluations demonstrate the effectiveness and superiority of our method over state-of-the-arts, in terms of noise removal and feature preservation.
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References
Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., & Silva, C. T. (2001). Point set surfaces. In Proceedings visualization, 2001. VIS’01 (pp. 21–29). IEEE.
Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., & Silva, C. T. (2003). Computing and rendering point set surfaces. IEEE Transactions on Visualization and Computer Graphics, 9(1), 3–15.
Avron, H., Sharf, A., Greif, C., & Cohen-Or, D. (2010). L1-sparse reconstruction of sharp point set surfaces. ACM Transactions on Graphics (TOG), 29(5), 1–12.
Chen, H., Wei, M., Sun, Y., Xie, X., & Wang, J. (2019). Multi-patch collaborative point cloud denoising via low-rank recovery with graph constraint. IEEE Transactions on Visualization and Computer Graphics, 26(11), 3255–3270.
Digne, J., Valette, S., & Chaine, R. (2017). Sparse geometric representation through local shape probing. IEEE Transactions on Visualization and Computer Graphics, 24(7), 2238–2250.
Fan, H., Su, H., & Guibas, L. J. (2017). A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 605–613.
Fleishman, S., Cohen-Or, D., & Silva, C. T. (2005). Robust moving least-squares fitting with sharp features. ACM Transactions on Graphics (TOG), 24(3), 544–552.
Gersho, A., & Gray, R. M. (2012). Vector quantization and signal compression (Vol. 159). Berlin.
Guo, Y., Bennamoun, M., Sohel, F., Lu, M., & Wan, J. (2014). An integrated framework for 3-d modeling, object detection, and pose estimation from point-clouds. IEEE Transactions on Instrumentation and Measurement, 64(3), 683–693.
Hermosilla, P., Ritschel, T., & Ropinski, T. (2019). Total denoising: Unsupervised learning of 3d point cloud cleaning. In Proceedings of the IEEE international conference on computer vision, pp. 52–60.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Hu, W., Gao, X., Cheung, G., & Guo, Z. (2020). Feature graph learning for 3d point cloud denoising. IEEE Transactions on Signal Processing, 68, 2841–2856.
Huang, C., Li, R., Li, X., & Fu, C. W. (2020). Non-local part-aware point cloud denoising. arXiv:200306631
Huang, H., Li, D., Zhang, H., Ascher, U., & Cohen-Or, D. (2009). Consolidation of unorganized point clouds for surface reconstruction. ACM Transactions on Graphics (TOG), 28(5), 1–7.
Huang, H., Wu, S., Gong, M., Cohen-Or, D., Ascher, U., & Zhang, H. (2013). Edge-aware point set resampling. ACM Transactions on Graphics (TOG), 32(1), 1–12.
Kong, D., Xu, L., Li, X., & Li, S. (2013). K-plane-based classification of airborne lidar data for accurate building roof measurement. IEEE Transactions on Instrumentation and Measurement, 63(5), 1200–1214.
Li, R., Li, X., Fu, C. W., Cohen-Or, D., & Heng, P. A. (2019). Pu-gan: a point cloud upsampling adversarial network. In Proceedings of the IEEE international conference on computer vision, pp. 7203–7212.
Li, X., Li, R., Zhu, L., Fu, C. W., & Heng, P. A. (2020). Dnf-net: a deep normal filtering network for mesh denoising. IEEE Transactions on Visualization and Computer Graphics, 27(10), 4060–4072.
Lipman, Y., Cohen-Or, D., Levin, D., & Tal-Ezer, H. (2007). Parameterization-free projection for geometry reconstruction. ACM Transactions on Graphics (TOG), 26(3), 22-es.
Liu, X., Han, Z., Liu, Y. S., & Zwicker, M. (2019). Point2sequence: Learning the shape representation of 3d point clouds with an attention-based sequence to sequence network. In Proceedings of the AAAI conference on artificial intelligence, (Vol. 33, 8778–8785).
Lu, D., Lu, X., Sun, Y., & Wang, J. (2020). Deep feature-preserving normal estimation for point cloud filtering. Computer-Aided Design, 125, 102860.
Lu, X., Schaefer, S., Luo, J., Ma, L., & He, Y. (2018). Low rank matrix approximation for geometry filtering. CoRR arXiv:abs/1803.06783.
Lu, X., Wu, S., Chen, H., Yeung, S. K., Chen, W., & Zwicker, M. (2017). Gpf: Gmm-inspired feature-preserving point set filtering. IEEE Transactions on Visualization and Computer Graphics, 24(8), 2315–2326.
Luo, S., & Hu, W. (2020). Differentiable manifold reconstruction for point cloud denoising. In Proceedings of the 28th ACM international conference on multimedia, pp. 1330–1338.
Nguyen, C. V., Izadi, S., & Lovell, D. (2012). Modeling kinect sensor noise for improved 3d reconstruction and tracking. In 2012 second international conference on 3D imaging, modeling, processing, visualization & transmission (pp. 524–530). IEEE.
Öztireli, A. C., Guennebaud, G., & Gross, M. (2009). Feature preserving point set surfaces based on non-linear kernel regression. Computer Graphics Forum, 28(2), 493–501.
Pistilli, F., Fracastoro, G., Valsesia, D., & Magli, E. (2020). Learning graph-convolutional representations for point cloud denoising. In European conference on computer vision (pp. 103–118). Springer.
Preiner, R., Mattausch, O., Arikan, M., Pajarola, R., & Wimmer, M. (2014). Continuous projection for fast l1 reconstruction. ACM Transactions on Graphics (TOG), 33(4), 47-1.
Qi, C. R., Su, H., Mo, K., & Guibas, L. J. (2017a). 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.
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017b). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (pp. 5099–5108). Red Hook, NY: Curran Associates Inc.
Rakotosaona, M., Barbera, V. L., Guerrero, P., Mitra, N. J., & Ovsjanikov, M. (2020). Pointcleannet: Learning to denoise and remove outliers from dense point clouds. Computer Graphics Forum, 39(1), 185–203.
Remil, O., Xie, Q., Xie, X., Xu, K., & Wang, J. (2017). Surface reconstruction with data-driven exemplar priors. Computer-Aided Design, 88, 31–41.
Rosman, G., Dubrovina, A., & Kimmel, R. (2013). Patch-collaborative spectral point-cloud denoising. Computer Graphics Forum, 32(8), 1–12.
Roveri, R., Öztireli, A. C., Pandele, I., & Gross, M. (2018). Pointpronets: Consolidation of point clouds with convolutional neural networks. Computer Graphics Forum, 37, 87–99.
Serna, A., Marcotegui, B., Goulette. F., & Deschaud, J. E. (2014). Paris-rue-madame database: A 3d mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods. In 4th International Conference on Pattern Recognition, Applications and Methods ICPRAM 2014.
Sun, Y., Schaefer, S., & Wang, W. (2015). Denoising point sets via l0 minimization. Computer Aided Geometric Design, 35, 2–15.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł, & Polosukhin, I. (2017). Attention is all you need. In I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), Advances in neural information processing systems (pp. 5998–6008). Red Hook, NY: Curran Associates Inc.
Wang, P. S., Liu, Y., & Tong, X. (2016). Mesh denoising via cascaded normal regression. ACM Transactions on Graphics, 35(6), 232–1.
Wang, Y., Liu, Y., Xie, Q., Wu, Q., Guo, X., Yu, Z., & Wang, J. (2020). Density-invariant registration of multiple scans for aircraft measurement. IEEE Transactions on Instrumentation and Measurement, 70, 1–15.
Wang, Y., Serena, F., Wu, S., Öztireli, C., & Sorkine-Hornung, O. (2019). Differentiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG), 38(6), 1–14.
Wei, M., Feng, Y., & Chen, H. (2020). Selective guidance normal filter for geometric texture removal. IEEE Transactions on Visualization and Computer Graphics, 27(12), 4469–4482.
Xie, Q., Lu, D., Huang, A., Yang, J., Li, D., Zhang, Y., & Wang, J. (2020). Rrcnet: Rivet region classification network for rivet flush measurement based on 3-d point cloud. IEEE Transactions on Instrumentation and Measurement, 70, 1–12.
Yu, L., Li, X., Fu, C. W., Cohen-Or, D., & Heng, P. A. (2018a). Ec-net: An edge-aware point set consolidation network. In Proceedings of the European conference on computer vision (ECCV), pp. 386–402.
Yu, L., Li, X., Fu, C. W., Cohen-Or, D., & Heng, P. A. (2018b). Pu-net: Point cloud upsampling network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2790–2799.
Zhang, D., Lu, X., Qin, H., & He, Y. (2020). Pointfilter: Point cloud filtering via encoder-decoder modeling. IEEE Transactions on Visualization and Computer Graphics.
Zhou, H., Chen, K., Zhang, W., Fang, H., Zhou, W., & Yu, N. (2019). Dup-net: Denoiser and upsampler network for 3d adversarial point clouds defense. In Proceedings of the IEEE international conference on computer vision, pp. 1961–1970.
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The work was supported by the Hong Kong Centre for Logistics Robotics.
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Communicated by Zuzana Kukelova.
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This work was supported in part by the National Key Research and Development Program of China (No. 2020YFB2010702, No. 2019YFB1707504), National Natural Science Foundation of China (No. 61772267, No. 62172218), Natural Science Foundation of Jiangsu Province (No. BK20190016), and the Free Exploration of Basic Research Project, Local Science and Technology Development Fund Guided by the Central Government of China (No. 2021Szvup060). The corresponding author for this paper is Jun Wang.
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Chen, H., Wei, Z., Li, X. et al. RePCD-Net: Feature-Aware Recurrent Point Cloud Denoising Network. Int J Comput Vis 130, 615–629 (2022). https://doi.org/10.1007/s11263-021-01564-7
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DOI: https://doi.org/10.1007/s11263-021-01564-7