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Automated assembly of 3D fractures is essential in orthopedics, archaeology, and our daily life.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
Martin A. Fischler and Robert C. Bolles · 1981
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Virtual archaeologist: Assembling the past
Georgios Papaioannou, E-A Karabassi, and Theoharis Theoharis · 2001
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On the automatic assemblage of arbitrary broken solid artefacts
Georgios Papaioannou and Evaggelia-Aggeliki Karabassi · 2003
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Reassembling fractured objects by geometric matching
Qi-Xing Huang, Simon Flöry, Natasha Gelfand, Michael Hofer, and Helmut Pottmann · 2006
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Computer-aided reconstruction and new matches in the forma urbis romae
David Koller and Marc Levoy · 2006
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Probabilistic graphical models: principles and techniques
Daphne Koller and Nir Friedman · 2009
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Multi-feature matching of fresco fragments
Corey Toler-Franklin, Benedict Brown, Tim Weyrich, Thomas Funkhouser, and Szymon Rusinkiewicz · 2010
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Learning how to match fresco fragments
Thomas Funkhouser, Hijung Shin, Corey Toler-Franklin, Antonio García Castañeda, Benedict Brown, David Dobkin, Szymon Rusinkiewicz, and Tim Weyrich · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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Robust reconstruction of indoor scenes
Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun · 2015
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A scalable active framework for region annotation in 3d shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
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3dmatch: Learning the matching of local 3d geometry in range scans
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and T Funkhouser · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Factor Graphs for Robot Perception
Frank Dellaert and Michael Kaess · 2017
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Deep learning of graph matching
Andrei Zanfir and Cristian Sminchisescu · 2018
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Deep learning of graph matching
Andrei Zanfir and Cristian Sminchisescu · 2018
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Open3d: A modern library for 3d data processing
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2018
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PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su · 2019
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Learning combinatorial embedding networks for deep graph matching
Runzhong Wang, Junchi Yan, and Xiaokang Yang · 2019
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Componet: Learning to generate the unseen by part synthesis and composition
Shonan rotation averaging: Global optimality by surfing s o ( n ) so(n)
Frank Dellaert, David M Rosen, Jing Wu, Robert Mahony, and Luca Carlone · 2020
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Automate: A dataset and learning approach for automatic mating of cad assemblies
Benjamin Jones, Dalton Hildreth, Duowen Chen, Ilya Baran, Vladimir G Kim, and Adriana Schulz · 2021
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Predator: Registration of 3d point clouds with low overlap
Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler · 2021
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LoFTR: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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Neural graph matching network: Learning lawler’s quadratic assignment problem with extension to hypergraph and multiple-graph matching
Runzhong Wang, Junchi Yan, and Xiaokang Yang · 2021
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Nadav Schor, Oren Katzir, Hao Zhang, and Daniel Cohen-Or · 2019
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Composite shape modeling via latent space factorization
Anastasia Dubrovina, Fei Xia, Panos Achlioptas, Mira Shalah, Raphael Groscot, and Leonidas J. Guibas · 2019
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Generative 3d part assembly via dynamic graph learning
Guanqi Zhan, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas J Guibas, Hao Dong, et al · 2020
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Learning 3d part assembly from a single image
Yichen Li, Kaichun Mo, Lin Shao, Minhyuk Sung, and Leonidas Guibas · 2020
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Coalesce: Component assembly by learning to synthesize connections
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri, Matthew Fisher, Vladimir G Kim, and Hao Zhang · 2020
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SuperGlue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Deep graph matching via blackbox differentiation of combinatorial solvers
Michal Rolínek, Paul Swoboda, Dominik Zietlow, Anselm Paulus, Vít Musil, and Georg Martius · 2020
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Lifa Zhu, Dongrui Liu, Changwei Lin, Rui Yan, Francisco Gómez-Fernández, Ninghua Yang, and Ziyong Feng · 2021
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Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun · 2021
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Breaking bad: A dataset for geometric fracture and reassembly
Silvia Sellán, Yun-Chun Chen, Ziyi Wu, Animesh Garg, and Alec Jacobson · 2022
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Rgl-net: A recurrent graph learning framework for progressive part assembly
Abhinav Narayan Harish, Rajendra Nagar, and Shanmuganathan Raman · 2022
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Joinable: Learning bottom-up assembly of parametric cad joints
Karl DD Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G Lambourne, Armando Solar-Lezama, et al · 2022
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Neural shape mating: Self-supervised object assembly with adversarial shape priors
Yun-Chun Chen, Haoda Li, Dylan Turpin, Alec Jacobson, and Animesh Garg · 2022
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Learning universe model for partial matching networks over multiple graphs
Zetian Jiang, Jiaxin Lu, Tianzhe Wang, and Junchi Yan · 2022
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Geometric transformer for fast and robust point cloud registration
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, and Kai Xu · 2022
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borglab/gtsam, May 2022
Frank Dellaert and GTSAM Contributors · 2022
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