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Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
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Sundar Vedula, Simon Baker, Peter Rander, Robert Collins, and Takeo Kanade · 1999
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A fast algorithm for computing the closest point and distance transform
Sean Mauch · 2000
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Shape priors for level set representations
Mikael Rousson and Nikos Paragios · 2002
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A linear time algorithm for computing exact euclidean distance transforms of binary images in arbitrary dimensions
Calvin R Maurer, Rensheng Qi, and Vijay Raghavan · 2003
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Level set based shape prior segmentation
Tony Chan and Wei Zhu · 2005
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Example-based 3D scan completion
Mark Pauly, Niloy J Mitra, Joachim Giesen, Markus H Gross, and Leonidas J Guibas · 2005
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Efficient 3d binary image skeletonization
Son Tran and Liwen Shih · 2005
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Optimal step nonrigid ICP algorithms for surface registration
Brian Amberg, Sami Romdhani, and Thomas Vetter · 2007
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Distance transform algorithms and their implementation and evaluation
George J Grevera · 2007
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A variational method for scene flow estimation from stereo sequences
Frédéric Huguet and Frédéric Devernay · 2007
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Geos: Geodesic image segmentation
Antonio Criminisi, Toby Sharp, and Andrew Blake · 2008
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Multi-scale 3D scene flow from binocular stereo sequences
Rui Li and Stan Sclaroff · 2008
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Chomp: Gradient optimization techniques for efficient motion planning
Nathan Ratliff, Matt Zucker, J Andrew Bagnell, and Siddhartha Srinivasa · 2009
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Kinecting the dots: Particle based scene flow from depth sensors
Simon Hadfield and Richard Bowden · 2011
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Distance transforms of sampled functions
Pedro F Felzenszwalb and Daniel P Huttenlocher · 2012
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Multi-view scene flow estimation: A view centered variational approach
Tali Basha, Yael Moses, and Nahum Kiryati · 2013
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Scene particles: Unregularized particle-based scene flow estimation
Simon Hadfield and Richard Bowden · 2013
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Go-icp: Solving 3d registration efficiently and globally optimally
Jiaolong Yang, Hongdong Li, and Yunde Jia · 2013
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SphereFlow: 6 DoF scene flow from RGB-D pairs
Michael Hornacek, Andrew Fitzgibbon, and Carsten Rother · 2014
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Dense semi-rigid scene flow estimation from rgbd images
Julian Quiroga, Thomas Brox, Frédéric Devernay, and James Crowley · 2014
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Motion planning under uncertainty for on-road autonomous driving
Wenda Xu, Jia Pan, Junqing Wei, and John M Dolan · 2014
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Object scene flow for autonomous vehicles
Moritz Menze and Andreas Geiger · 2015
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Self-supervised learning of non-rigid residual flow and ego-motion
Ivan Tishchenko, Sandro Lombardi, Martin Oswald, and Marc Pollefeys · 2020
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Deep distance transform for tubular structure segmentation in ct scans
Yan Wang, Xu Wei, Fengze Liu, Jieneng Chen, Yuyin Zhou, Wei Shen, Elliot K Fishman, and Alan L Yuille · 2020
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FlowNet3D++: Geometric losses for deep scene flow estimation
Zirui Wang, Shuda Li, Henry Howard-Jenkins, Victor Prisacariu, and Min Chen · 2020
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PointPWC-Net: Cost volume on point clouds for (self-) supervised scene flow estimation
Wenxuan Wu, Zhi Yuan Wang, Zhuwen Li, Wei Liu, and Li Fuxin · 2020
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Upgrading optical flow to 3D scene flow through optical expansion
Gengshan Yang and Deva Ramanan · 2020
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Slim: Self-supervised lidar scene flow and motion segmentation
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox · 2016
Cited alongside, same era.
A point set generation network for 3D object reconstruction from a single image
Haoqiang Fan, Hao Su, and Leonidas J Guibas · 2017
Cited alongside, same era.
Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
Cited alongside, same era.
Motion-based object segmentation based on dense RGB-D scene flow
Lin Shao, Parth Shah, Vikranth Dwaracherla, and Jeannette Bohg · 2018
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Deepigeos: a deep interactive geodesic framework for medical image segmentation
Guotai Wang, Maria A Zuluaga, Wenqi Li, Rosalind Pratt, Premal A Patel, Michael Aertsen, Tom Doel, Anna L David, Jan Deprest, Sébastien Ourselin, et al · 2018
Cited alongside, same era.
Mono-SF: Multi-view geometry meets single-view depth for monocular scene flow estimation of dynamic traffic scenes
Fabian Brickwedde, Steffen Abraham, and Rudolf Mester · 2019
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Stefan Andreas Baur, David Josef Emmerichs, Frank Moosmann, Peter Pinggera, Björn Ommer, and Andreas Geiger · 2021
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Fastnerf: High-fidelity neural rendering at 200fps
Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin · 2021
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Weakly supervised learning of rigid 3d scene flow
Zan Gojcic, Or Litany, Andreas Wieser, Leonidas J Guibas, and Tolga Birdal · 2021
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Baking neural radiance fields for real-time view synthesis
Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec · 2021
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Self-supervised multi-frame monocular scene flow
Junhwa Hur and Stefan Roth · 2021
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Scalable scene flow from point clouds in the real world
Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, and Jonathon Shlens · 2021
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FlowStep3D: Model unrolling for self-supervised scene flow estimation
Yair Kittenplon, Yonina C Eldar, and Dan Raviv · 2021
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Neural scene flow prior
Xueqian Li, Jhony Kaesemodel Pontes, and Simon Lucey · 2021
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Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps
Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger · 2021
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RAFT-3D: Scene flow using rigid-motion embeddings
Zachary Teed and Jia Deng · 2021
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Festa: Flow estimation via spatial-temporal attention for scene point clouds
Haiyan Wang, Jiahao Pang, Muhammad A Lodhi, Yingli Tian, and Dong Tian · 2021
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Pv-raft: point-voxel correlation fields for scene flow estimation of point clouds
Yi Wei, Ziyi Wang, Yongming Rao, Jiwen Lu, and Jie Zhou · 2021
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St3d: Self-training for unsupervised domain adaptation on 3d object detection
Jihan Yang, Shaoshuai Shi, Zhe Wang, Hongsheng Li, and Xiaojuan Qi · 2021
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Plenoxels: Radiance fields without neural networks
Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa · 2021
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Plenoctrees for real-time rendering of neural radiance fields
Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa · 2021
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Tensorf: Tensorial radiance fields
Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su · 2022
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Exploiting rigidity constraints for lidar scene flow estimation
Guanting Dong, Yueyi Zhang, Hanlin Li, Xiaoyan Sun, and Zhiwei Xiong · 2022
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Deformation and correspondence aware unsupervised synthetic-to-real scene flow estimation for point clouds
Zhao Jin, Yinjie Lei, Naveed Akhtar, Haifeng Li, and Munawar Hayat · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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Motion inspired unsupervised perception and prediction in autonomous driving
Mahyar Najibi, Jingwei Ji, Yin Zhou, Charles R Qi, Xinchen Yan, Scott Ettinger, and Dragomir Anguelov · 2022
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Neural prior for trajectory estimation
Chaoyang Wang, Xueqian Li, Jhony Kaesemodel Pontes, and Simon Lucey · 2022
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Trading positional complexity vs deepness in coordinate networks
Jianqiao Zheng, Sameera Ramasinghe, Xueqian Li, and Simon Lucey · 2022
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Rsf: Optimizing rigid scene flow from 3d point clouds without labels
David Deng and Avideh Zakhor · 2023
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