SHREC 2022: Fitting and recognition of simple geometric primitives on point clouds

Kaiser, 2019, A survey of simple geometric primitives detection methods for captured 3D data, Comput Graph Forum, 38, 167, 10.1111/cgf.13451 Li, 2019, Supervised fitting of geometric primitives to 3D point clouds, 2647 Raffo, 2020, Reverse engineering of CAD models via clustering and approximate implicitization, Comput Aided Geom Design, 80, 10.1016/j.cagd.2020.101876 Sharma, 2020, ParSeNet: A parametric surface fitting network for 3D point clouds, 261 Yan, 2021, HPNet: Deep primitive segmentation using hybrid representations, 2733 Saporta, 2022, Unsupervised recursive deep fitting of 3D primitives to points, Comput Graph, 102, 289, 10.1016/j.cag.2021.10.020 Koch, 2019, ABC: A big CAD model dataset for geometric deep learning Romanengo, 2022, Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects, Comput Graph, 102, 133, 10.1016/j.cag.2021.09.013 Sharma, 2018, CSGNet: Neural shape parser for constructive solid geometry Kuhn, 2018 Raffo, 2022, Fitting and recognition of geometric primitives in segmented 3D point clouds using a localized voting procedure, Comput Aided Geom Design, 10.1016/j.cagd.2022.102123 Beltrametti, 2012, An algebraic approach to Hough transforms, J Algebra, 37, 669, 10.1016/j.jalgebra.2012.09.012 Beltrametti, 2020, Moore–penrose approach in the hough transform framework, Appl Math Comput, 375 Limberger, 2015, Real-time detection of planar regions in unorganized point clouds, Pattern Recognit, 48, 2043, 10.1016/j.patcog.2014.12.020 Tang, 2013, Histogram of oriented normal vectors for object recognition with a depth sensor, 525 Wahl, 2003, Surflet-pair-relation histograms: a statistical 3D-shape representation for rapid classification, 474 Schnabel, 2007, Efficient RANSAC for point-cloud shape detection, Comput Graph Forum, 26, 214, 10.1111/j.1467-8659.2007.01016.x Charles, 2017, PointNet: Deep learning on point sets for 3D classification and segmentation, 77 Choy, 2019, 4D spatio-temporal ConvNets: Minkowski convolutional neural networks Wu, 2015, 3D ShapeNets: A deep representation for volumetric shapes Kingma, 2015, Adam: A method for stochastic optimization Krizhevsky, 2017, ImageNet classification with deep convolutional neural networks, Commun ACM, 60, 84, 10.1145/3065386 Qie, 2020, Toward a mathematical definition of reconstruction operation for iso GPS standards, Procedia CIRP, 92, 152, 10.1016/j.procir.2020.05.183 Shakarji, 1998, Least-squares fitting algorithms of the NIST algorithm testing system, J Res Natl Inst Stand Technol