thesis.bbl

\begin{thebibliography}{100}

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Eren~Erdal Aksoy, Saimir Baci, and Selcuk Cavdar.
\newblock Salsanet: Fast road and vehicle segmentation in lidar point clouds
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\newblock In {\em IEEE intelligent vehicles symposium (IV)}, 2020.

\bibitem{aoki2019pointnetlk}
Yasuhiro Aoki, Hunter Goforth, Rangaprasad~Arun Srivatsan, and Simon Lucey.
\newblock {PointnetLK}: Robust \& efficient point cloud registration using
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\bibitem{attaiki2021dpfm}
Souhaib Attaiki, Gautam Pai, and Maks Ovsjanikov.
\newblock Dpfm: Deep partial functional maps.
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\bibitem{attal2023hyperreel}
Benjamin Attal, Jia-Bin Huang, Christian Richardt, Michael Zollhoefer, Johannes
  Kopf, Matthew O'Toole, and Changil Kim.
\newblock {HyperReel}: High-fidelity {6-DoF} video with ray-conditioned
  sampling.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{attal2021torf}
Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt,
  James Tompkin, and Matthew O'Toole.
\newblock {T{\"o}RF}: Time-of-flight radiance fields for dynamic scene view
  synthesis.
\newblock {\em Advances in Neural Information Processing Systems (NeurIPS)},
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\bibitem{aygun20214d}
Mehmet Aygun, Aljosa Osep, Mark Weber, Maxim Maximov, Cyrill Stachniss, Jens
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\newblock 4d panoptic {LiDAR} segmentation.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{bai2020d3feat}
Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, and Chiew-Lan Tai.
\newblock D3feat: Joint learning of dense detection and description of 3d local
  features.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{barron2021mip}
Jonathan~T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo
  Martin-Brualla, and Pratul~P Srinivasan.
\newblock {Mip-NeRF}: A multiscale representation for anti-aliasing neural
  radiance fields.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2021.

\bibitem{barron2022mip}
Jonathan~T Barron, Ben Mildenhall, Dor Verbin, Pratul~P Srinivasan, and Peter
  Hedman.
\newblock {Mip-NeRF} 360: Unbounded anti-aliased neural radiance fields.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2022.

\bibitem{barron2023zipnerf}
Jonathan~T. Barron, Ben Mildenhall, Dor Verbin, Pratul~P. Srinivasan, and Peter
  Hedman.
\newblock Zip-nerf: Anti-aliased grid-based neural radiance fields.
\newblock In {\em IEEE International Conference on Computer Vision (ICCV)},
  2023.

\bibitem{baur2021slim}
Stefan~Andreas Baur, David~Josef Emmerichs, Frank Moosmann, Peter Pinggera,
  Bjorn Ommer, and Andreas Geiger.
\newblock {SLIM}: Self-supervised {LiDAR} scene flow and motion segmentation.
\newblock In {\em IEEE International Conference on Computer Vision (ICCV)},
  2021.

\bibitem{behl2019pointflownet}
Aseem Behl, Despoina Paschalidou, Simon Donn{\'e}, and Andreas Geiger.
\newblock {PointFlowNet}: Learning representations for rigid motion estimation
  from point clouds.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{behley2019semantickitti}
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill
  Stachniss, and Jurgen Gall.
\newblock {SemanticKITTI}: A dataset for semantic scene understanding of lidar
  sequences.
\newblock In {\em IEEE International Conference on Computer Vision (ICCV)},
  2019.

\bibitem{bengio2009curriculum}
Yoshua Bengio, J{\'e}r{\^o}me Louradour, Ronan Collobert, and Jason Weston.
\newblock Curriculum learning.
\newblock In {\em IEEE International Conference on Machine Learning (ICML)},
  2009.

\bibitem{berman2018lovasz}
Maxim Berman, Amal~Rannen Triki, and Matthew~B Blaschko.
\newblock The {Lov{\'a}sz-Softmax} loss: A tractable surrogate for the
  optimization of the intersection-over-union measure in neural networks.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2018.

\bibitem{besl1992method}
Paul~J Besl and Neil~D McKay.
\newblock Method for registration of 3-d shapes.
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\bibitem{bijelic2020seeing}
Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus
  Dietmayer, and Felix Heide.
\newblock Seeing through fog without seeing fog: Deep multimodal sensor fusion
  in unseen adverse weather.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{buehler2001unstructured}
Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael
  Cohen.
\newblock Unstructured lumigraph rendering.
\newblock In {\em Computer graphics and interactive techniques}, 2001.

\bibitem{caccia2019deep}
Lucas Caccia, Herke Van~Hoof, Aaron Courville, and Joelle Pineau.
\newblock Deep generative modeling of {LiDAR} data.
\newblock In {\em IEEE/RSJ Intl. Conference on Intelligent Robots and Systems
  (IROS)}, 2019.

\bibitem{caesar2020nuscenes}
Holger Caesar, Varun Bankiti, Alex~H Lang, Sourabh Vora, Venice~Erin Liong,
  Qiang Xu, Anush Krishnan, Yu~Pan, Giancarlo Baldan, and Oscar Beijbom.
\newblock {nuScenes}: A multimodal dataset for autonomous driving.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{carlsson2001signature}
Tomas Carlsson, Ove Steinvall, and Dietmar Letalick.
\newblock Signature simulation and signal analysis for 3-d laser radar.
\newblock Technical report, Swedish Defence Research Agency, 2001.

\bibitem{chabra2020deep}
Rohan Chabra, Jan~E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven
  Lovegrove, and Richard Newcombe.
\newblock Deep local shapes: Learning local {SDF} priors for detailed 3d
  reconstruction.
\newblock In {\em European Conference on Computer Vision (ECCV)}, 2020.

\bibitem{chang2015shapenet}
Angel~X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang,
  Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et~al.
\newblock {ShapeNet}: An information-rich 3d model repository.
\newblock {\em arXiv preprint arXiv:1512.03012}, 2015.

\bibitem{chang2023neural}
MingFang Chang, Akash Sharma, Michael Kaess, and Simon Lucey.
\newblock Neural radiance field with {LiDAR} maps.
\newblock In {\em IEEE International Conference on Computer Vision (ICCV)},
  2023.

\bibitem{chen2022tensorf}
Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su.
\newblock {TensorRF}: Tensorial radiance fields.
\newblock In {\em European Conference on Computer Vision (ECCV)}, 2022.

\bibitem{chen2021mvsnerf}
Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu,
  and Hao Su.
\newblock Mvsnerf: Fast generalizable radiance field reconstruction from
  multi-view stereo.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2021.

\bibitem{chen2016dynamic}
Chi Chen and Bisheng Yang.
\newblock Dynamic occlusion detection and inpainting of in situ captured
  terrestrial laser scanning point clouds sequence.
\newblock {\em ISPRS Journal of Photogrammetry and Remote Sensing}, 2016.

\bibitem{chen2023view}
Shenchang~Eric Chen and Lance Williams.
\newblock View interpolation for image synthesis.
\newblock In {\em Seminal Graphics Papers: Pushing the Boundaries}. 2023.

\bibitem{chen2021ral}
X.~Chen, S.~Li, B.~Mersch, L.~Wiesmann, J.~Gall, J.~Behley, and C.~Stachniss.
\newblock Moving object segmentation in {3D} {LiDAR} data: A learning-based
  approach exploiting sequential data.
\newblock {\em IEEE Robotics and Automation Letters (RA-L)}, 2021.

\bibitem{chen2019learning}
Zhiqin Chen and Hao Zhang.
\newblock Learning implicit fields for generative shape modeling.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{chng2022garf}
Shin-Fang Chng, Sameera Ramasinghe, Jamie Sherrah, and Simon Lucey.
\newblock {GARF}: Gaussian activated radiance fields for high fidelity
  reconstruction and pose estimation.
\newblock {\em arXiv preprint arXiv:2204.05735}, 2022.

\bibitem{choi2015robust}
Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun.
\newblock Robust reconstruction of indoor scenes.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2015.

\bibitem{choy2020deep}
Christopher Choy, Wei Dong, and Vladlen Koltun.
\newblock Deep global registration.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{choy2019Minkowski}
Christopher Choy, JunYoung Gwak, and Silvio Savarese.
\newblock 4d spatio-temporal convnets: Minkowski convolutional neural networks.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{Choy2019FCGF}
Christopher Choy, Jaesik Park, and Vladlen Koltun.
\newblock Fully convolutional geometric features.
\newblock In {\em IEEE International Conference on Computer Vision (ICCV)},
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\bibitem{curless1996volumetric}
Brian Curless and Marc Levoy.
\newblock A volumetric method for building complex models from range images.
\newblock In {\em ACM SIGGRAPH}, 1996.

\bibitem{cuturi2013sinkhorn}
Marco Cuturi.
\newblock Sinkhorn distances: Lightspeed computation of optimal transport.
\newblock {\em Advances in Neural Information Processing Systems (NeurIPS)},
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\bibitem{dai2017scannet}
Angela Dai, Angel~X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser,
  and Matthias Nie{\ss}ner.
\newblock Scannet: Richly-annotated 3d reconstructions of indoor scenes.
\newblock In {\em The IEEE Conference on Computer Vision and Pattern
  Recognition (CVPR)}, 2017.

\bibitem{debevec2023modeling}
Paul~E Debevec, Camillo~J Taylor, and Jitendra Malik.
\newblock Modeling and rendering architecture from photographs: A hybrid
  geometry-and image-based approach.
\newblock In {\em Seminal Graphics Papers: Pushing the Boundaries}. 2023.

\bibitem{deitke2024objaverse}
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya
  Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir~Yitzhak Gadre,
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\newblock Objaverse-xl: A universe of 10m+ 3d objects.
\newblock {\em Advances in Neural Information Processing Systems (NeurIPS)},
  2024.

\bibitem{delitzas2024scenefun3d}
Alexandros Delitzas, Ayca Takmaz, Federico Tombari, Robert Sumner, Marc
  Pollefeys, and Francis Engelmann.
\newblock Scenefun3d: Fine-grained functionality and affordance understanding
  in 3d scenes.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2024.

\bibitem{dendorfer2021motchallenge}
Patrick Dendorfer, Aljosa Osep, Anton Milan, Konrad Schindler, Daniel Cremers,
  Ian Reid, Stefan Roth, and Laura Leal-Taix{\'e}.
\newblock {MOTchallenge}: A benchmark for single-camera multiple target
  tracking.
\newblock {\em International Journal of Computer Vision (IJCV)}, 2021.

\bibitem{Deng2018PPFFoldNetUL}
Haowen Deng, Tolga Birdal, and Slobodan Ilic.
\newblock {PPF-FoldNet}: Unsupervised learning of rotation invariant 3d local
  descriptors.
\newblock In {\em European Conference on Computer Vision (ECCV)}, 2018.

\bibitem{deng2018ppfnet}
Haowen Deng, Tolga Birdal, and Slobodan Ilic.
\newblock Ppfnnet: Global context aware local features for robust 3d point
  matching.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2018.

\bibitem{deng2009imagenet}
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li~Fei-Fei.
\newblock Imagenet: A large-scale hierarchical image database.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2009.

\bibitem{deng2021depth}
Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan.
\newblock Depth-supervised {NeRF}: Fewer views and faster training for free.
\newblock {\em arXiv preprint arXiv:2107.02791}, 2021.

\bibitem{kangle2021dsnerf}
Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan.
\newblock Depth-supervised {NeRF}: Fewer views and faster training for free.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2022.

\bibitem{detone2018superpoint}
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich.
\newblock Superpoint: Self-supervised interest point detection and description.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR) Workshops}, 2018.

\bibitem{dewan2016rigid}
Ayush Dewan, Tim Caselitz, Gian~Diego Tipaldi, and Wolfram Burgard.
\newblock Rigid scene flow for 3d lidar scans.
\newblock In {\em IEEE/RSJ Intl. Conference on Intelligent Robots and Systems
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\bibitem{dosovitskiy2015flownet}
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas,
  Vladimir Golkov, Patrick Van Der~Smagt, Daniel Cremers, and Thomas Brox.
\newblock {FlowNet}: Learning optical flow with convolutional networks.
\newblock In {\em IEEE International Conference on Computer Vision (ICCV)},
  2015.

\bibitem{dosovitskiy2017carla}
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen
  Koltun.
\newblock {CARLA}: An open urban driving simulator.
\newblock In {\em Conference on Robot Learning (CoRL)}, 2017.

\bibitem{dusmanu2019d2Net}
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic,
  Akihiko Torii, and Torsten Sattler.
\newblock {D2-Net}: A trainable {CNN} for joint detection and description of
  local features.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{ehret2024radar}
Thibaud Ehret, Roger Mar{\'\i}, Dawa Derksen, Nicolas Gasnier, and Gabriele
  Facciolo.
\newblock Radar fields: An extension of radiance fields to sar.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{ester1996density}
Martin Ester, Hans-Peter Kriegel, J{\"o}rg Sander, Xiaowei Xu, et~al.
\newblock A density-based algorithm for discovering clusters in large spatial
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\newblock In {\em Proc.\ KDD}, 1996.

\bibitem{fan2021point}
Hehe Fan, Yi~Yang, and Mohan Kankanhalli.
\newblock Point 4d transformer networks for spatio-temporal modeling in point
  cloud videos.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2021.

\bibitem{fan2020pstnet}
Hehe Fan, Xin Yu, Yuhang Ding, Yi~Yang, and Mohan Kankanhalli.
\newblock {PSTNet}: Point spatio-temporal convolution on point cloud sequences.
\newblock In {\em International Conference on Learning Representations (ICLR)},
  2020.

\bibitem{fan2023fsdv2}
Lue Fan, Feng Wang, Naiyan Wang, and Zhaoxiang Zhang.
\newblock {FSD V2}: Improving fully sparse 3d object detection with virtual
  voxels.
\newblock {\em arXiv preprint arXiv:2308.03755}, 2023.

\bibitem{baidusim}
Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu~Ma, Liang
  Wang, and Ruigang Yang.
\newblock Augmented {LiDAR} simulator for autonomous driving.
\newblock {\em IEEE Robotics and Automation Letters (RA-L)}, 2020.

\bibitem{fang2020augmented}
Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu~Ma, Liang
  Wang, and Ruigang Yang.
\newblock Augmented lidar simulator for autonomous driving.
\newblock {\em IEEE Robotics and Automation Letters (RA-L)}, 2020.

\bibitem{filatov2020any}
Artem Filatov, Andrey Rykov, and Viacheslav Murashkin.
\newblock Any motion detector: Learning class-agnostic scene dynamics from a
  sequence of lidar point clouds.
\newblock In {\em IEEE International Conference on Robotics and Automation
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\bibitem{ransac}
Martin~A. Fischler and Robert~C. Bolles.
\newblock Random sample consensus: a paradigm for model fitting with
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\newblock {\em Commun. ACM}, 1981.

\bibitem{flynn2019deepview}
John Flynn, Michael Broxton, Paul Debevec, Matthew DuVall, Graham Fyffe, Ryan
  Overbeck, Noah Snavely, and Richard Tucker.
\newblock Deepview: View synthesis with learned gradient descent.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{fridovich2022plenoxels}
Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and
  Angjoo Kanazawa.
\newblock Plenoxels: Radiance fields without neural networks.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{geiger2012we}
Andreas Geiger, Philip Lenz, and Raquel Urtasun.
\newblock Are we ready for autonomous driving? the kitti vision benchmark
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\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{geiger2012kitti}
Andreas Geiger, Philip Lenz, and Raquel Urtasun.
\newblock Are we ready for autonomous driving? the kitti vision benchmark
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\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{genova2020local}
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser.
\newblock Local deep implicit functions for 3d shape.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{giancola2019leveraging}
Silvio Giancola, Jesus Zarzar, and Bernard Ghanem.
\newblock Leveraging shape completion for 3d siamese tracking.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{gilmer2017neural}
Justin Gilmer, Samuel~S Schoenholz, Patrick~F Riley, Oriol Vinyals, and
  George~E Dahl.
\newblock Neural message passing for quantum chemistry.
\newblock In {\em IEEE International Conference on Machine Learning (ICML)},
  2017.

\bibitem{glennie2012calibration}
Craig Glennie.
\newblock Calibration and kinematic analysis of the {Velodyne} {HDL-64E} {S2}
  lidar sensor.
\newblock {\em Photogrammetric Engineering \& Remote Sensing}, 78(4):339--347,
  2012.

\bibitem{gojcic2021weakly}
Zan Gojcic, Or~Litany, Andreas Wieser, Leonidas~J Guibas, and Tolga Birdal.
\newblock Weakly supervised learning of rigid 3d scene flow.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2021.

\bibitem{gojcic2020learning}
Zan Gojcic, Caifa Zhou, Jan~D Wegner, Leonidas~J Guibas, and Tolga Birdal.
\newblock Learning multiview 3d point cloud registration.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{gojcic2020multiview}
Zan Gojcic, Caifa Zhou, Jan~D Wegner, Leonidas~J Guibas, and Tolga Birdal.
\newblock Learning multiview 3d point cloud registration.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2020.

\bibitem{gojcic20193DSmoothNet}
Zan Gojcic, Caifa Zhou, Jan~D Wegner, and Andreas Wieser.
\newblock The perfect match: 3d point cloud matching with smoothed densities.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2019.

\bibitem{gojcic2018learned}
Zan Gojcic, Caifa Zhou, and Andreas Wieser.
\newblock Learned compact local feature descriptor for {TLS}-based geodetic
  monitoring of natural outdoor scenes.
\newblock In {\em ISPRS Annals}, 2018.

\bibitem{3DSemanticSegmentationWithSubmanifoldSparseConvNet}
Benjamin Graham, Martin Engelcke, and Laurens van~der Maaten.
\newblock 3d semantic segmentation with submanifold sparse convolutional
  networks.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2018.

\bibitem{icml2020_2086}
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman.
\newblock Implicit geometric regularization for learning shapes.
\newblock In {\em IEEE International Conference on Machine Learning (ICML)},
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\bibitem{gross2019alignnet}
Johannes Gro{\ss}, Aljo{\v{s}}a O{\v{s}}ep, and Bastian Leibe.
\newblock {AlignNet-3D}: Fast point cloud registration of partially observed
  objects.
\newblock In {\em IEEE International conference on 3D vision (3DV)}, 2019.

\bibitem{gu2020weakly}
Jiayuan Gu, Wei-Chiu Ma, Sivabalan Manivasagam, Wenyuan Zeng, Zihao Wang, Yuwen
  Xiong, Hao Su, and Raquel Urtasun.
\newblock Weakly-supervised 3d shape completion in the wild.
\newblock In {\em European Conference on Computer Vision (ECCV)}, 2020.

\bibitem{guillard2022learning}
Benoit Guillard, Sai Vemprala, Jayesh~K Gupta, Ondrej Miksik, Vibhav Vineet,
  Pascal Fua, and Ashish Kapoor.
\newblock Learning to simulate realistic {LiDARs}.
\newblock In {\em IEEE/RSJ Intl. Conference on Intelligent Robots and Systems
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\bibitem{guo2014performanceEvaluation}
Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, Min Lu, Jianwei Wan, and Jun
  Zhang.
\newblock Performance evaluation of 3{D} local feature descriptors.
\newblock In {\em Asian Conference on Computer Vision (ACCV)}, 2014.

\bibitem{guo2020deep}
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li~Liu, and Mohammed Bennamoun.
\newblock Deep learning for 3d point clouds: A survey.
\newblock {\em IEEE transactions on pattern analysis and machine intelligence},
  2020.

\bibitem{hackel2020inference}
Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan~D Wegner, and Konrad
  Schindler.
\newblock Inference, learning and attention mechanisms that exploit and
  preserve sparsity in {CNNs}.
\newblock {\em International Journal of Computer Vision (IJCV)}, 2020.

\bibitem{hahner2022lidar}
Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu,
  Dengxin Dai, and Luc Van~Gool.
\newblock Lidar snowfall simulation for robust 3d object detection.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2022.

\bibitem{hahner2021fog}
Martin Hahner, Christos Sakaridis, Dengxin Dai, and Luc {Van Gool}.
\newblock Fog simulation on real {LiDAR} point clouds for 3d object detection
  in adverse weather.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2021.

\bibitem{Halber2016StructuredGR}
Maciej Halber and Thomas~A. Funkhouser.
\newblock Structured global registration of {RGB-D} scans in indoor
  environments.
\newblock {\em arXiv preprint arXiv:1607.08539}, 2016.

\bibitem{hanocka2019meshcnn}
Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, and Daniel
  Cohen-Or.
\newblock Meshcnn: a network with an edge.
\newblock {\em ACM Transactions on Graphics (ToG)}, 2019.

\bibitem{henzler2018single}
Phlipp Henzler, Volker Rasche, Timo Ropinski, and Tobias Ritschel.
\newblock Single-image tomography: 3d volumes from 2d cranial x-rays.
\newblock In {\em Computer Graphics Forum}, 2018.

\bibitem{hu2020randla}
Qingyong Hu, Bo~Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki
  Trigoni, and Andrew Markham.
\newblock {RandLA-Net}: Efficient semantic segmentation of large-scale point
  clouds.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
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\bibitem{huang2022multiway}
Jiahui Huang, Tolga Birdal, Zan Gojcic, Leonidas~J Guibas, and Shi-Min Hu.
\newblock Multiway non-rigid point cloud registration via learned functional
  map synchronization.
\newblock {\em IIEEE Trans. on Pattern Analysis and Machine Intelligence
  (PAMI)}, 2022.

\bibitem{huang2021multibodysync}
Jiahui Huang, He~Wang, Tolga Birdal, Minhyuk Sung, Federica Arrigoni, Shi-Min
  Hu, and Leonidas~J Guibas.
\newblock {MultiBodySync}: Multi-body segmentation and motion estimation via 3d
  scan synchronization.
\newblock In {\em IEEE Conference on Computer Vision and Pattern Recognition
  (CVPR)}, 2021.

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