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We present a review of 3D point cloud processing and learning for autonomous driving.
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“Efficient variants of the ICP algorithm,”
S. Rusinkiewicz and M. Levoy, · 2001
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“Technology development for army unmanned ground vehicles,”
National Research Council, · 2002
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“Computing and rendering point set surfaces,”
M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin, and C. T. Silva, · 2003
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“Linear least-squares optimization for point-toplane icp surface registration,”
K-L. Low, · 2004
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“Geometry-guided progressive lossless 3D mesh coding with octree (OT) decomposition,”
J. Peng and C.-C. Jay Kuo, · 2005
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“A robust algorithm for point set registration using mixture of gaussians,”
B. Jian and B. C. Vemuri, · 2005
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“Augmented reality scouting for interactive 3d reconstruction,”
B. Reitinger, C. Zach, and D. Schmalstieg, · 2007
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“Fast poisson disk sampling in arbitrary dimensions,”
R. Bridson, · 2007
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“K-means++: the advantages of careful seeding,”
D. Arthur and S. Vassilvitskii, · 2007
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“Reconstructing animated meshes from time-varying point clouds,”
J. Süßmuth, M. Winter, and G. Greiner, · 2008
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“4-points congruent sets for robust pairwise surface registration,”
D. Aiger, N. J. Mitra, and D. Cohen-Or, · 2008
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“Aligning point cloud views using persistent feature histograms,”
R. B. Rusu, N. Blodow, Z. C. Marton, and M. Beetz, · 2008
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“Autonomous driving in urban environments: Boss and the urban challenge,”
C. Urmson, J. Anhalt, D. Bagnell, C. R. Baker, R. Bittner, M. N. Clark, J. M. Dolan, D. Duggins, T. Galatali, C. Geyer, M. Gittleman, S. Harbaugh, M. Hebert, T. M. Howard, S. Kolski, A. Kelly, M. Likhachev, M. McNaughton, N. Miller, K. M. Peterson, B. Pilnick, R. Rajkumar, P. E. Rybski, B. Salesky, Y-W. Seo, S. Singh, J. M. Snider, A. Stentz, W. Whittaker, Z. Wolkowicki, J. Ziglar, H. Bae, T. Brown, D. Demitrish, B. Litkouhi, J. Nickolaou, V. Sadekar, W. Zhang, J. Struble, M. Taylor, M. Darms, and D. Ferguson, · 2009
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“Bilateral filtering: Theory and applications,”
P. Kornprobst, J. Tumblin, and F. Durand, · 2009
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“Fast point feature histograms (fpfh) for 3d registration,”
R. B. Rusu, N. Blodow, and M. Beetz, · 2009
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“A tutorial on graph-based SLAM,”
G. Grisetti, R. Kümmerle, C. Stachniss, and W. Burgard, · 2010
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“Point cloud non local denoising using local surface descriptor similarity,”
J.-E. Deschaud and F. Goulette, · 2010
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“Point set registration: Coherent point drift,”
A. Myronenko and X. Song, · 2010
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“Unique signatures of histograms for local surface description,”
F. Tombari, S. Salti, and L. Di Stefano, · 2010
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“Kinectfusion: real-time 3D reconstruction and interaction using a moving depth camera,”
S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, and A. Davison, · 2011
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“Multiview registration via graph diffusion of dual quaternions,”
A. Torsello, E. Rodola, and A. Albarelli, · 2011
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“Are we ready for autonomous driving? the KITTI vision benchmark suite,”
A. Geiger, P. Lenz, and R. Urtasun, · 2012
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“Challenging data sets for point cloud registration algorithms,”
F. Pomerleau, M. Liu, F. Colas, and R. Siegwart, · 2012
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“A benchmark for the evaluation of rgb-d slam systems,”
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, · 2012
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“Vision meets robotics: The kitti dataset,”
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, · 2013
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“Edge-aware point set resampling,”
H. Huang, S. Wu, M. Gong, D. Cohen-Or, U. Ascher, and H. Zhang, · 2013
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“Road marking detection using LIDAR reflective intensity data and its application to vehicle localization,”
A. Y. Hata and D. F. Wolf, · 2014
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“Super 4PCS fast global pointcloud registration via smart indexing,”
N. Mellado, D. Aiger, and N. J. Mitra, · 2014
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“A generative model for the joint registration of multiple point sets,”
G. D. Evangelidis, D. Kounades-Bastian, R. Horaud, and E. Z. Psarakis, · 2014
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“A review of point cloud registration algorithms for mobile robotics,”
F. Pomerleau, F. Colas, and R. Siegwart, · 2015
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“Automated as-built 3d reconstruction of civil infrastructure using computer vision: Achievements, opportunities, and challenges,”
H. Fathi, F. Dai, and M. I. A. Lourakis, · 2015
Cited alongside, same era.
“Pde-based graph signal processing for 3-d color point clouds : Opportunities for cultural herihe arts and found promising,”
F. Lozes, A. Elmoataz, and O. Lezoray, · 2015
Cited alongside, same era.
“Shapenet: An information-rich 3d model repository,”
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q-X. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, · 2015
Cited alongside, same era.
“Non-rigid 3d shape retrieval,”
Z. Lian, J. Zhang, S. Choi, H. ElNaghy, J. El-Sana, T. Furuya, A. Giachetti, R. A. Güler, L. Lai, C. Li, H. Li, F. A. Limberger, R. R. Martin, R. Umino Nakanishi, A. Neto, L. Gustavo Nonato, R. Ohbuchi, K. Pevzner, D. Pickup, P. L. Rosin, A. Sharf, L. Sun, X. Sun, S. Tari, G. B. Ünal, and R. C. Wilson, · 2015
Cited alongside, same era.
“Deep points consolidation,”
S. Wu, H. Huang, M. Gong, M. Zwicker, and D. Cohen-Or, · 2015
“PointFusion: Deep sensor fusion for 3d bounding box estimation,”
D. Xu, D. Anguelov, and A. Jain, · 2018
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“Deep continuous fusion for multi-sensor 3d object detection,”
M. Liang, B. Yang, S. Wang, and R. Urtasun, · 2018
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“Learning representations and generative models for 3d point clouds,”
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. J. Guibas, · 2018
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“Foldingnet: Point cloud auto-encoder via deep grid deformation,”
Y. Yang, C. Feng, Y. Shen, and D. Tian, · 2018
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“A papier-mâché approach to learning 3d surface generation,”
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry, · 2018
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“3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model,”
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Cited alongside, same era.
“Globally consistent registration of terrestrial laser scans via graph optimization,”
P. W. Theiler, J. D. Wegner, and K. Schindler, · 2015
Cited alongside, same era.
“Robust reconstruction of indoor scenes,”
C. Sungjoon, Q. Zhou, and V. Koltun, · 2015
Cited alongside, same era.
“Vehicle detection from 3d lidar using fully convolutional network,”
B. Li, T. Zhang, and T. Xia, · 2016
Cited alongside, same era.
“SSD: single shot multibox detector,”
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C-Y. Fu, and A. C. Berg, · 2016
Cited alongside, same era.
“Go-icp: A globally optimal solution to 3d ICP point-set registration,”
J. Yang, H. Li, D. Campbell, and Y. Jia, · 2016
Cited alongside, same era.
“A scalable active framework for region annotation in 3d shape collections,”
L. Yi, V. G. Kim, D. Ceylan, I-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. J. Guibas, · 2016
Cited alongside, same era.
“A probabilistic framework for color-based point set registration,”
Martin Danelljan, Giulia Meneghetti, Fahad Shahbaz Khan, and Michael Felsberg, · 2016
Cited alongside, same era.
J. Zeng, G. Cheung, M. Ng, and C. Yang J. Pang, · 2018
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“Weighted multi-projection: 3d point cloud denoising with estimated tangent planes,”
C. Duan, S. Chen, and J. Kovacevic, · 2018
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O. Dovrat, I. Lang, and S. Avidan, · 2018
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“Patch-based progressive 3d point set upsampling,”
Y. Wang, S. Wu, H. Huang, D. Cohen-Or, and O. Sorkine-Hornung, · 2018
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“Pu-net: Point cloud upsampling network,”
L. Yu, X. Li, C-W. Fu, D. Cohen-Or, and P-A. Heng, · 2018
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“Ec-net: An edge-aware point set consolidation network,”
L. Yu, X. Li, C-W. Fu, D. Cohen-Or, and P-A. Heng, · 2018
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“Ppfnet: Global context aware local features for robust 3d point matching,”
H. Deng, T. Birdal, and S. Ilic, · 2018
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“3dfeat-net: Weakly supervised local 3d features for point cloud registration,”
Z-J. Yew and G-H. Lee, · 2018
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“MapNet: An allocentric spatial memory for mapping environments,”
J. F. Henriques and A. Vedaldi, · 2018
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“CodeSLAM - learning a compact, optimisable representation for dense visual SLAM,”
M. Bloesch, J. Czarnowski, R. Clark, S. Leutenegger, and A. J. Davison, · 2018
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“DeepTAM: Deep tracking and mapping,”
H. Zhou, B. Ummenhofer, and T. Brox, · 2018
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“Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry,”
N. Yang, R. Wang, J. Stückler, and D. Cremers, · 2018
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“Global pose estimation with an attention-based recurrent network,”
E. Parisotto, D. Singh Chaplot, J. Zhang, and R. Salakhutdinov, · 2018
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“Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks,”
A. Taeihagh and H. Si Min Lim, · 2019
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“Self-driving cars: A survey,” arXiv:1901.04407 [cs.RO], Jan. 2019
C. Badue, R. Guidolini, R. Vivacqua Carneiro, P. Azevedo, V. Brito Cardoso, A. Forechi, L. Ferreira Reis Jesus, R. Ferreira Berriel, T. Meireles Paixão, F. Mutz, T. Oliveira-Santos, and A. Ferreira De Souza, · 2019
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“Lasernet: An efficient probabilistic 3d object detector for autonomous driving,”
G. P. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez, and C. K. Wellington, · 2019
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“Dynamic graph CNN for learning on point clouds,”
Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, and J. M. Solomon, · 2019
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“Large-scale 3d point cloud representations via graph inception networks with applications to autonomous driving,”
S. Chen, S. Niu, T. Lan, and B. Liu, · 2019
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“DeepGCNs: Can GCNs go as deep as CNNs?,”
G. Li, M. Müller, A. K. Thabet, and B. Ghanem, · 2019
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“PointRCNN: 3d object proposal generation and detection from point cloud,”
S. Shi, X. Wang, and H. Li, · 2019
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“Fvnet: 3d front-view proposal generation for real-time object detection from point clouds,”
J. Zhou, X. Lu, X. Tan, Z. Shao, S. Ding, and L. Ma, · 2019
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S. Shi, Z. Wang, X. Wang, and H. Li, · 2019
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“Second: Sparsely embedded convolutional detection,”
Y. Yan, Y. Mao, and B. Li, · 2019
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“Multi-task multi-sensor fusion for 3d object detection,”
M. Liang, B. Yang, Y. Chen, R. Hu, and R. Urtasun, · 2019
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“Sensor fusion for joint 3d object detection and semantic segmentation,”
G. P. Meyer, J. Charland, D. Hegde, A. Laddha, and C. Vallespi-Gonzalez, · 2019
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“Emerging MPEG standards for point cloud compression,”
S. Schwarz, M. Preda, V. Baroncini, M. Budagavi, P. César, P. A. Chou, R. A. Cohen, M. Krivokuca, S. Lasserre, Z. Li, J. Llach, K. Mammou, R. Mekuria, O. Nakagami, E. Siahaan, A. J. Tabatabai, A. M. Tourapis, and V. Zakharchenko, · 2019
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“3d point cloud denoising via deep neural network based local surface estimation,”
C. Duan, S. Chen, and J. Kovačević, · 2019
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GraphPointNet: Graph Convolutional NeuralNetwork for Point Cloud Denoising
F. Pistilli, · 2019
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“Deepmapping: Unsupervised map estimation from multiple point clouds,”
L. Ding and C. Feng, · 2019
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“Deepicp: An end-to-end deep neural network for 3d point cloud registration,”
W. Lu, G. Wan, Y. Zhou, X. Fu, P. Yuan, and S. Song, · 2019
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“Deep unsupervised learning of 3d point clouds via graph topology inference and filtering,”
S. Chen, C. Duan, Y. Yang, D. Li, C. Feng, and D. Tian, · 2020
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