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Although recent efforts have extended Neural Radiance Fields (NeRF) into LiDAR point cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed poses.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
Martin A Fischler and Robert C Bolles · 1981
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Method for registration of 3-d shapes
Paul J Besl and Neil D McKay · 1992
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Efficient variants of the icp algorithm
Szymon Rusinkiewicz and Marc Levoy · 2001
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan Koenig and Andrew Howard · 2004
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Global motion estimation from point matches
Mica Arie-Nachimson, Shahar Z Kovalsky, Ira Kemelmacher-Shlizerman, Amit Singer, and Ronen Basri · 2012
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Comparing icp variants on real-world data sets: Open-source library and experimental protocol
François Pomerleau, Francis Colas, Roland Siegwart, and Stéphane Magnenat · 2013
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A theory of minimal 3d point to 3d plane registration and its generalization
Srikumar Ramalingam and Yuichi Taguchi · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Robust reconstruction of indoor scenes
Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun · 2015
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Spectral synchronization of multiple views in se (3)
Federica Arrigoni, Beatrice Rossi, and Andrea Fusiello · 2016
Earlier work this paper cites.
Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
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Cad priors for accurate and flexible instance reconstruction
Tolga Birdal and Slobodan Ilic · 2017
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Practical and efficient multi-view matching
Eleonora Maset, Federica Arrigoni, and Andrea Fusiello · 2017
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Airsim: High-fidelity visual and physical simulation for autonomous vehicles
Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Earlier work this paper cites.
Pointnetlk: Robust & efficient point cloud registration using pointnet
Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, and Simon Lucey · 2019
Earlier work this paper cites.
Probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope
Tolga Birdal and Umut Simsekli · 2019
Earlier work this paper cites.
Fully convolutional geometric features
Christopher Choy, Jaesik Park, and Vladlen Koltun · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Earlier work this paper cites.
Deep closest point: Learning representations for point cloud registration
Yue Wang and Justin M Solomon · 2019
Earlier work this paper cites.
nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
Cited alongside, same era.
Learning multiview 3d point cloud registration
Zan Gojcic, Caifa Zhou, Jan D Wegner, Leonidas J Guibas, and Tolga Birdal · 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.
Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration
Jiahao Li, Changhao Zhang, Ziyao Xu, Hangning Zhou, and Chi Zhang · 2020
Cited alongside, same era.
Lidarsim: Realistic lidar simulation by leveraging the real world
Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun · 2020
Cited alongside, same era.
Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction
Zehao Yu, Songyou Peng, Michael Niemeyer, Torsten Sattler, and Andreas Geiger · 2022
Later among the works it cites.
Ray priors through reprojection: Improving neural radiance fields for novel view extrapolation
Jian Zhang, Yuanqing Zhang, Huan Fu, Xiaowei Zhou, Bowen Cai, Jinchi Huang, Rongfei Jia, Binqiang Zhao, and Xing Tang · 2022
Later among the works it cites.
Nope-nerf: Optimising neural radiance field with no pose prior
Wenjing Bian, Zirui Wang, Kejie Li, Jia-Wang Bian, and Victor Adrian Prisacariu · 2023
Later among the works it cites.
Dbarf: Deep bundle-adjusting generalizable neural radiance fields
Yu Chen and Gim Hee Lee · 2023
Later among the works it cites.
Nerf-loam: Neural implicit representation for large-scale incremental lidar odometry and mapping
Junyuan Deng, Qi Wu, Xieyuanli Chen, Songpengcheng Xia, Zhen Sun, Guoqing Liu, Wenxian Yu, and Ling Pei · 2023
Later among the works it cites.
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Reluplex made more practical: Leaky relu
Jin Xu, Zishan Li, Bowen Du, Miaomiao Zhang, and Jing Liu · 2020
Cited alongside, same era.
Deepgmr: Learning latent gaussian mixture models for registration
Wentao Yuan, Benjamin Eckart, Kihwan Kim, Varun Jampani, Dieter Fox, and Jan Kautz · 2020
Cited alongside, same era.
Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan · 2021
Cited alongside, same era.
Barf: Bundle-adjusting neural radiance fields
Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey · 2021
Cited alongside, same era.
Hregnet: A hierarchical network for large-scale outdoor lidar point cloud registration
Fan Lu, Guang Chen, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, and Rongqi Gu · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Michael Oechsle, Songyou Peng, and Andreas Geiger · 2021
Cited alongside, same era.
Robust camera pose refinement for multi-resolution hash encoding
Hwan Heo, Taekyung Kim, Jiyoung Lee, Jaewon Lee, Soohyun Kim, Hyunwoo J Kim, and Jin-Hwa Kim · 2023
Later among the works it cites.
Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields
Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang, Lin Gao, Xiao Liu, and Yuewen Ma · 2023
Later among the works it cites.
Neural lidar fields for novel view synthesis
Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad Schindler, and Or Litany · 2023
Later among the works it cites.
Pcgen: Point cloud generator for lidar simulation
Chenqi Li, Yuan Ren, and Bingbing Liu · 2023
Later among the works it cites.
Parallel inversion of neural radiance fields for robust pose estimation
Yunzhi Lin, Thomas Müller, Jonathan Tremblay, Bowen Wen, Stephen Tyree, Alex Evans, Patricio A Vela, and Stan Birchfield · 2023
Later among the works it cites.
Robust dynamic radiance fields
Yu-Lun Liu, Chen Gao, Andreas Meuleman, Hung-Yu Tseng, Ayush Saraf, Changil Kim, Yung-Yu Chuang, Johannes Kopf, and Jia-Bin Huang · 2023
Later among the works it cites.
Camp: Camera preconditioning for neural radiance fields
Keunhong Park, Philipp Henzler, Ben Mildenhall, Jonathan T Barron, and Ricardo Martin-Brualla · 2023
Later among the works it cites.
Sc-nerf: Self-correcting neural radiance field with sparse views
Liang Song, Guangming Wang, Jiuming Liu, Zhenyang Fu, Yanzi Miao, et al · 2023
Later among the works it cites.
Lidar-nerf: Novel lidar view synthesis via neural radiance fields
Tang Tao, Longfei Gao, Guangrun Wang, Peng Chen, Dayang Hao, Xiaodan Liang, Mathieu Salzmann, and Kaicheng Yu · 2023
Later among the works it cites.
Rotation synchronization via deep matrix factorization
GK Tejus, Giacomo Zara, Paolo Rota, Andrea Fusiello, Elisa Ricci, and Federica Arrigoni · 2023
Later among the works it cites.
Sparf: Neural radiance fields from sparse and noisy poses
Prune Truong, Marie-Julie Rakotosaona, Fabian Manhardt, and Federico Tombari · 2023
Later among the works it cites.
Robust multiview point cloud registration with reliable pose graph initialization and history reweighting
Haiping Wang, Yuan Liu, Zhen Dong, Yulan Guo, Yu-Shen Liu, Wenping Wang, and Bisheng Yang · 2023
Later among the works it cites.
Unisim: A neural closed-loop sensor simulator
Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, and Raquel Urtasun · 2023
Later among the works it cites.
Nerf-lidar: Generating realistic lidar point clouds with neural radiance fields
Junge Zhang, Feihu Zhang, Shaochen Kuang, and Li Zhang · 2023
Later among the works it cites.
Multiway point cloud mosaicking with diffusion and global optimization
Shengze Jin, Iro Armeni, Marc Pollefeys, and Daniel Barath · 2024
Closest in time.
Hdmnet: A hierarchical matching network with double attention for large-scale outdoor lidar point cloud registration
Weiyi Xue, Fan Lu, and Guang Chen · 2024
Closest in time.
Nerf-lidar: Generating realistic lidar point clouds with neural radiance fields
Junge Zhang, Feihu Zhang, Shaochen Kuang, and Li Zhang · 2024
Closest in time.
Lidar4d: Dynamic neural fields for novel space-time view lidar synthesis
Zehan Zheng, Fan Lu, Weiyi Xue, Guang Chen, and Changjun Jiang · 2024
Closest in time.