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Multi-view implicit scene reconstruction methods have become increasingly popular due to their ability to represent complex scene details.
The determination of next best views
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A next-best-view algorithm for 3d scene recovery with 5 degrees of freedom
Robert Fisher and José Miguel Sanchiz · 1999
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A solution to the next best view problem for automated surface acquisition
Richard Pito · 1999
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An adaptive hierarchical next-best-view algorithm for 3d reconstruction of indoor scenes
Kok-Lim Low and Anselmo Lastra · 2006
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Data acquisition and view planning for 3-d modeling tasks
Paul S Blaer and Peter K Allen · 2007
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A next-best-view algorithm for autonomous 3d object modeling by a humanoid robot
Torea Foissotte, Olivier Stasse, Adrien Escande, and Abderrahmane Kheddar · 2008
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Active perception: Interactive manipulation for improving object detection
Quoc V Le, Ashutosh Saxena, and Andrew Y Ng · 2008
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Image quality metrics: Psnr vs. ssim
Alain Hore and Djemel Ziou · 2010
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Autonomous generation of complete 3d object models using next best view manipulation planning
Michael Krainin, Brian Curless, and Dieter Fox · 2011
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Bayesian active object recognition via gaussian process regression
Marco F Huber, Tobias Dencker, Masoud Roschani, and Jürgen Beyerer · 2012
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Self-training with selection-by-rejection
Yan Zhou, Murat Kantarcioglu, and Bhavani Thuraisingham · 2012
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Hierarchical ray tracing for fast volumetric next-best-view planning
J Irving Vasquez-Gomez, L Enrique Sucar, and Rafael Murrieta-Cid · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Volumetric next-best-view planning for 3d object reconstruction with positioning error
J Irving Vasquez-Gomez, L Enrique Sucar, Rafael Murrieta-Cid, and Efrain Lopez-Damian · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Supervised learning of the next-best-view for 3d object reconstruction
Miguel Mendoza, J Irving Vasquez-Gomez, Hind Taud, L Enrique Sucar, and Carolina Reta · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Viewpoints planning for active 3-d reconstruction of profiled blades using estimated occupancy probabilities (eop)
Weixing Peng, Yaonan Wang, Zhiqiang Miao, Mingtao Feng, and Yongpeng Tang · 2020
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Next-best view policy for 3d reconstruction
Daryl Peralta, Joel Casimiro, Aldrin Michael Nilles, Justine Aletta Aguilar, Rowel Atienza, and Rhandley Cajote · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
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Pixel2mesh: Generating 3d mesh models from single rgb images
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang · 2018
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Sharing deep neural network models with interpretation
Huijun Wu, Chen Wang, Jie Yin, Kai Lu, and Liming Zhu · 2018
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Active object reconstruction using a guided view planner
Xin Yang, Yuanbo Wang, Yaru Wang, Baocai Yin, Qiang Zhang, Xiaopeng Wei, and Hongbo Fu · 2018
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Abc: A big cad model dataset for geometric deep learning
Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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Machine Learning Refined: Foundations, Algorithms, and Applications
Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos · 2020
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Multiview neural surface reconstruction by disentangling geometry and appearance
Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Basri Ronen, and Yaron Lipman · 2020
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Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su · 2021
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Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Michael Oechsle, Songyou Peng, and Andreas Geiger · 2021
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Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny · 2021
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Active 3d shape reconstruction from vision and touch
Edward Smith, David Meger, Luis Pineda, Roberto Calandra, Jitendra Malik, Adriana Romero Soriano, and Michal Drozdzal · 2021
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Next-best-view regression using a 3d convolutional neural network
J Irving Vasquez-Gomez, David Troncoso, Israel Becerra, Enrique Sucar, and Rafael Murrieta-Cid · 2021
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pixelnerf: Neural radiance fields from one or few images
Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa · 2021
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