Fetching the paper…
Reading the bibliography…
State-of-the-art 3D point cloud registration methods rely on labeled 3D datasets for training, which limits their practical applications in real-world scenarios and often hinders generalization to unseen scenes.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
Earlier work this paper cites.
Rover visual obstacle avoidance
Hans P Moravec · 1981
Earlier work this paper cites.
A method for registration of 3-D shapes
P.J. Besl and N.D. McKay · 1992
Earlier work this paper cites.
Using spin images for efficient object recognition in cluttered 3d scenes
Andrew E Johnson and Martial Hebert · 1999
Earlier work this paper cites.
Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
Earlier work this paper cites.
Surf: Speeded up robust features
Herbert Bay, Tinne Tuytelaars, and Luc Van Gool · 2006
Earlier work this paper cites.
A survey for the quadratic assignment problem
Eliane Maria Loiola, Nair Maria Maia De Abreu, Paulo Oswaldo Boaventura-Netto, Peter Hahn, and Tania Querido · 2007
Earlier work this paper cites.
4-points congruent sets for robust pairwise surface registration
Dror Aiger, Niloy J Mitra, and Daniel Cohen-Or · 2008
Earlier work this paper cites.
Aligning point cloud views using persistent feature histograms
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton, and Michael Beetz · 2008
Earlier work this paper cites.
Fast point feature histograms (fpfh) for 3d registration
Radu Bogdan Rusu, Nico Blodow, and Michael Beetz · 2009
Earlier work this paper cites.
Model globally, match locally: Efficient and robust 3d object recognition
Bertram Drost, Markus Ulrich, Nassir Navab, and Slobodan Ilic · 2010
Earlier work this paper cites.
Unique shape context for 3D data description
F. Tombari, S. Salti, and L. Di Stefano · 2010
Earlier work this paper cites.
Orb: An efficient alternative to sift or surf
Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski · 2011
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
Earlier work this paper cites.
Mcov: a covariance descriptor for fusion of texture and shape features in 3d point clouds
Pol Cirujeda, Xavier Mateo, Yashin Dicente, and Xavier Binefa · 2014
Earlier work this paper cites.
Shot: Unique signatures of histograms for surface and texture description
Samuele Salti, Federico Tombari, and Luigi Di Stefano · 2014
Earlier work this paper cites.
A stereo vision approach for cooperative robotic movement therapy
Benjamin Busam, Marco Esposito, Simon Che’Rose, Nassir Navab, and Benjamin Frisch · 2015
Earlier work this paper cites.
Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
Earlier work this paper cites.
3dmatch: Learning local geometric descriptors from rgb-d reconstructions
Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser · 2017
Earlier work this paper cites.
Superpoint: Self-supervised interest point detection and description
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2018
Cited alongside, same era.
Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
Cited alongside, same era.
Open3d: A modern library for 3d data processing
Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun · 2018
Cited alongside, same era.
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
Cited alongside, same era.
The perfect match: 3d point cloud matching with smoothed densities
Zan Gojcic, Caifa Zhou, Jan D Wegner, and Andreas Wieser · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Loftr: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
Later among the works it cites.
Cofinet: Reliable coarse-to-fine correspondences for robust point cloud registration
Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, and Slobodan Ilic · 2021
Later among the works it cites.
Improving rgb-d point cloud registration by learning multi-scale local linear transformation
Mohamed El Banani and Justin Johnson · 2022
Later among the works it cites.
You only hypothesize once: Point cloud registration with rotation-equivariant descriptors
Haiping Wang, Yuan Liu, Zhen Dong, and Wenping Wang · 2022
Later among the works it cites.
Riga: Rotation-invariant and globally-aware descriptors for point cloud registration
Hao Yu, Ji Hou, Zheng Qin, Mahdi Saleh, Ivan Shugurov, Kai Wang, Benjamin Busam, and Slobodan Ilic · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Learning an Effective Equivariant 3D Descriptor Without Supervision
R. Spezialetti, S. Salti, and L. Di Stefano · 2019
Cited alongside, same era.
3D point capsule networks
Y. Zhao, T. Birdal, H. Deng, and F. Tombari · 2019
Cited alongside, same era.
D3feat: Joint learning of dense detection and description of 3d local features
Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, and Chiew-Lan Tai · 2020
Cited alongside, same era.
Feature-metric registration: A fast semi-supervised approach for robust point cloud registration without correspondences
Xiaoshui Huang, Guofeng Mei, and Jian Zhang · 2020
Cited alongside, same era.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
Cited alongside, same era.
Teaser: Fast and certifiable point cloud registration
Heng Yang, Jingnan Shi, and Luca Carlone · 2020
Cited alongside, same era.
Point-tta: Test-time adaptation for point cloud registration using multitask meta-auxiliary learning
Ahmed Hatem, Yiming Qian, and Yang Wang · 2023
Closest in time.
Conceptfusion: Open-set multimodal 3d mapping
Krishna Murthy Jatavallabhula, Alihusein Kuwajerwala, Qiao Gu, Mohd Omama, Tao Chen, Alaa Maalouf, Shuang Li, Ganesh Iyer, Soroush Saryazdi, Nikhil Keetha, et al · 2023
Closest in time.
Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
Closest in time.
Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi · 2023
Closest in time.
Openscene: 3d scene understanding with open vocabularies, 2023
Songyou Peng, Kyle Genova, Chiyu ”Max” Jiang, Andrea Tagliasacchi, Marc Pollefeys, and Thomas Funkhouser · 2023
Closest in time.
Geotransformer: Fast and robust point cloud registration with geometric transformer
Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Slobodan Ilic, Dewen Hu, and Kai Xu · 2023
Closest in time.
Rotation-invariant transformer for point cloud matching
Hao Yu, Zheng Qin, Ji Hou, Mahdi Saleh, Dongsheng Li, Benjamin Busam, and Slobodan Ilic · 2023
Closest in time.
Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Ziyao Zeng, Zipeng Qin, Shanghang Zhang, and Peng Gao · 2023
Closest in time.
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration , page 149–167
Xueyang Kang, Zhaoliang Luan, Kourosh Khoshelham, and Bing Wang · 2024
Closest in time.
Shapesplat: A large-scale dataset of gaussian splats and their self-supervised pretraining
Qi Ma, Yue Li, Bin Ren, Nicu Sebe, Ender Konukoglu, Theo Gevers, Luc Van Gool, and Danda Pani Paudel · 2024
Closest in time.
Sam 2: Segment anything in images and videos
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, et al · 2024
Closest in time.
Bringing masked autoencoders explicit contrastive properties for point cloud self-supervised learning
Bin Ren, Guofeng Mei, Danda Pani Paudel, Weijie Wang, Yawei Li, Mengyuan Liu, Rita Cucchiara, Luc Van Gool, and Nicu Sebe · 2024
Closest in time.
Florence-2: Advancing a unified representation for a variety of vision tasks
Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Ce Liu, and Lu Yuan · 2024
Closest in time.