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Many problems in computer vision require dealing with sparse, unordered data in the form of point clouds.
Inhibition in the Eye of Limulus
H K. Hartline, Henry G. Wagner, and Floyd Ratliff · 1956
Earlier work this paper cites.
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.
Least Median of Squares Regression
Peter J. Rousseeuw · 1984
Earlier work this paper cites.
Performance Evaluation of a Class of M-estimators for Surface Parameter Estimation in Noisy Range Data
Muhammad J Mirza and Kim L Boyer · 1993
Earlier work this paper cites.
Multiple View Geometry in Computer Vision
Richard Hartley and Andrew Zisserman · 2000
Earlier work this paper cites.
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Philip H.S. Torr and Andrew Zisserman · 2000
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Alex Krizhevsky, Ilya Sutskever, and Geoffery E. Hinton · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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First-Person Hyper-Lapse Videos
Johannes Kopf, Michael F Cohen, and Richard Szeliski · 2014
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Jimmy L. Ba, Jamie R. Kiros, and Geoffrey E. Hinton · 2016
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