Fit4CAD: A point cloud benchmark for fitting simple geometric primitives in CAD objects

Computers and Graphics - Tập 102 - Trang 133-143 - 2022
Chiara Romanengo1, Andrea Raffo1,2, Yifan Qie3, Nabil Anwer3, Bianca Falcidieno1
1Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes”, Consiglio Nazionale delle Ricerche, Via de Marini 6, 16149 Genova, Italy
2Department of Mathematics, University of Oslo, Moltke Moes vei 35, 0851 Oslo, Norway
3Automated Production Research Laboratory (LURPA), ENS Paris-Saclay, Université Paris-Saclay, 91190 Gif-sur-Yvette, France

Tài liệu tham khảo

Kaiser, 2019, A survey of simple geometric primitives detection methods for captured 3D data, Comput Graph Forum, 38, 167, 10.1111/cgf.13451 Tal, 2007, Mesh retrieval by components, 44, 10.1007/978-3-540-75274-5_3 Qi, 2017, Pointnet: Deep learning on point sets for 3D classification and segmentation, 77 Ganapathi-Subramanian, 2018, Parsing geometry using structure-aware shape templates, 672 Smeaton, 2006, Evaluation campaigns and trecvid, 321 Bojer, 2021, Kaggle forecasting competitions: An overlooked learning opportunity, Int J Forecast, 37, 587, 10.1016/j.ijforecast.2020.07.007 Veltkamp, 2006 Chen, 2009, A benchmark for 3D mesh segmentation, ACM Trans Graph, 28, 10.1145/1531326.1531379 Lavoué, 2012, Shrec’12 track: 3D mesh segmentation Zhou Q, Jacobson A. Thingi10k: A dataset of 10, 000 3d-printing models, CoRR 2016;abs/1605.04797, http://arxiv.org/abs/1605.04797. Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X et al. 3d shapenets: A deep representation for volumetric shapes. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, p. 1912–20. The Shape Repository, http://visionair.ge.imati.cnr.it/ontologies/shapes/; 2011–2015. Koch S, Matveev A, Jiang Z, Williams F, Artemov A, Burnaev E et al. ABC: A big CAD model dataset for geometric deep learning. In: The IEEE conference on computer vision and pattern recognition (CVPR). 2019. Geuzaine, 2009, Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities, Int J Num Meth Eng, 79, 1309, 10.1002/nme.2579 Mathur, 2020, Interactive programming for parametric CAD, Comput Graph Forum, 39, 408, 10.1111/cgf.14046 Dokken, 1997 Barrowclough, 2012, Approximate implicitization using linear algebra, J Appl Math, 10.1155/2012/293746 Kuhn, 2018 Deza, 2009 Hough, 1962 Qie, 2021, Enhanced invariance class partitioning using discrete curvatures and conformal geometry, Comput Aided Des, 133, 10.1016/j.cad.2020.102985 Pauly, 2002, Efficient simplification of point-sampled surfaces, 163 2011 Gelfand, 2004, Shape segmentation using local slippage analysis, 214 Schnabel, 2007, Efficient RANSAC for point-cloud shape detection, Comput Graph Forum, 26, 214, 10.1111/j.1467-8659.2007.01016.x 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 Edelsbrunner, 2002, Topological persistence and simplification, Discrete Comput Geom, 28, 511, 10.1007/s00454-002-2885-2 Biasotti, 2016, Tracking the evolution of rainfall precipitation fields using persistent maxima, 29 Tulsiani, 2017, Learning shape abstractions by assembling volumetric primitives