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PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion.
Voxelnet: End-to-end learning for point cloud based 3d object detection
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Method for registration of 3-d shapes
P. J. Besl and N. D. McKay · 1992
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The farthest point strategy for progressive image sampling
Y. Eldar, M. Lindenbaum, M. Porat, and Y. Y. Zeevi · 1997
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Efficient variants of the ICP algorithm
S. Rusinkiewicz and M. Levoy · 2001
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A multi-resolution scheme ICP algorithm for fast shape registration
T. Jost and H. Hugli · 2002
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Lucas-Kanade 20 years on: A unifying framework
S. Baker and I. Matthews · 2004
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Robust global registration
N. Gelfand, N. J. Mitra, L. J. Guibas, and H. Pottmann · 2005
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Fully automatic registration of 3D point clouds
A. Makadia, A. Patterson, and K. Daniilidis · 2006
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Fast point feature histograms (FPFH) for 3D registration
R. B. Rusu, N. Blodow, and M. Beetz · 2009
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One point isometric matching with the heat kernel
M. Ovsjanikov, Q. Mérigot, F. Mémoli, and L. Guibas · 2010
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Monte carlo pose estimation with quaternion kernels and the distribution
J. Glover, G. Bradski, and R. B. Rusu · 2012
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3D object recognition in cluttered scenes with local surface features: a survey
Y. Guo, M. Bennamoun, F. Sohel, M. Lu, and J. Wan · 2014
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Convex relaxations of SE(2) and SE(3) for visual pose estimation
M. B. Horowitz, N. Matni, and J. W. Burdick · 2014
Cited alongside, same era.
Dense semantic correspondence where every pixel is a classifier
H. Bristow, J. Valmadre, and S. Lucey · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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3d semantic parsing of large-scale indoor spaces
I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese · 2016
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Learning to track at 100 fps with deep regression networks
PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes
Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox · 2017
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Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition
M. Angelina Uy and G. Hee Lee · 2018
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Probabilistic pose estimation using a Bingham distribution-based linear filter
R. Arun Srivatsan, M. Xu, N. Zevallos, and H. Choset · 2018
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Fast and accurate point cloud registration using trees of gaussian mixtures
B. Eckart, K. Kim, and J. Kautz · 2018
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Matching RGB Images to CAD Models for Object Pose Estimation
G. Georgakis, S. Karanam, Z. Wu, and J. Kosecka · 2018
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D. Held, S. Thrun, and S. Savarese · 2016
Cited alongside, same era.
Point registration via efficient convex relaxation
H. Maron, N. Dym, I. Kezurer, S. Kovalsky, and Y. Lipman · 2016
Cited alongside, same era.
Go-ICP: A globally optimal solution to 3D ICP point-set registration
J. Yang, H. Li, D. Campbell, and Y. Jia · 2016
Cited alongside, same era.
3d point cloud registration for localization using a deep neural network auto-encoder
G. Elbaz, T. Avraham, and A. Fischer · 2017
Cited alongside, same era.
A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
Cited alongside, same era.
Globally Optimal Object Pose Estimation in Point Clouds with Mixed-Integer Programming
G. Izatt, H. Dai, and R. Tedrake · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
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Point reg net: Invariant features for point cloud registration using in image-guided radiation therapy
Z. Ma, B. Liu, F. Zhou, and J. Chen · 2018
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Frustum pointnets for 3d object detection from RGB-D data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2018
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Inverse composition discriminative optimization for point cloud registration
J. Vongkulbhisal, B. Irastorza Ugalde, F. De la Torre, and J. P. Costeira · 2018
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Dynamic graph CNN for learning on point clouds
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon · 2018
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3dfeat-net: Weakly supervised local 3d features for point cloud registration
Z. J. Yew and G. H. Lee · 2018
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Iterative transformer network for 3d point cloud
W. Yuan, D. Held, C. Mertz, and M. Hebert · 2018
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PCN: Point Completion Network
W. Yuan, T. Khot, D. Held, C. Mertz, and M. Hebert · 2018
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PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
Y. Aoki, H. Goforth, R. A. Srivatsan, and S. Lucey · 2019
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