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An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets.
3D urban scene modeling integrating recognition and reconstruction
Nico Cornelis, Bastian Leibe, Kurt Cornelis, and Luc Van Gool · 2008
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Contextual classification with functional max-margin markov networks
Daniel Munoz, J Andrew Bagnell, Nicolas Vandapel, and Martial Hebert · 2009
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Grasping novel objects with depth segmentation
Deepak Rao, Quoc V Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsang, and Andrew Y Ng · 2010
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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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The ISPRS benchmark on urban object classification and 3D building reconstruction
Franz Rottensteiner, Gunho Sohn, Jaewook Jung, Markus Gerke, Caroline Baillard, Sebastien Benitez, and Uwe Breitkopf · 2012
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Indoor segmentation and support inference from RGB-D images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Unsupervised feature learning for classification of outdoor 3D scans
Mark De Deuge, Alastair Quadros, Calvin Hung, and Bertrand Douillard · 2013
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Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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Paris-rue-madame database: a 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods
Andrés Serna, Beatriz Marcotegui, François Goulette, and Jean-Emmanuel Deschaud · 2014
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ShapeNet: An information-rich 3D model repository
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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Sun RGB-D: A RGB-D scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
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TerraMobilita/iQmulus urban point cloud analysis benchmark
Bruno Vallet, Mathieu Brédif, Andrés Serna, Beatriz Marcotegui, and Nicolas Paparoditis · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Virtual worlds as proxy for multi-object tracking analysis
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig · 2016
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SceneNet: understanding real world indoor scenes with synthetic data
A Handa, V Patraucean, V Badrinarayanan, S Stent, and R Cipolla · 2016
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SceneNet RGB-D: 5m photorealistic images of synthetic indoor trajectories with ground truth
John McCormac, Ankur Handa, Stefan Leutenegger, and Andrew J Davison · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M Lopez · 2016
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A scalable active framework for region annotation in 3D shape collections
Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas · 2016
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Joint 2D-3D-semantic data for indoor scene understanding
Iro Armeni, Sasha Sax, Amir R Zamir, and Silvio Savarese · 2017
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ScanNet: Richly-annotated 3D reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Semantic3D.Net: A new large-scale point cloud classification benchmark
Timo Hackel, Nikolay Savinov, Lubor Ladicky, Jan D Wegner, Konrad Schindler, and Marc Pollefeys · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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PointNet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas · 2017
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Adapnet: Adaptive semantic segmentation in adverse environmental conditions
Abhinav Valada, Johan Vertens, Ankit Dhall, and Wolfram Burgard · 2017
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Scene parsing through ADE20K dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2017
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The lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks
Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko · 2018
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3D semantic segmentation with submanifold sparse convolutional networks
Benjamin Graham, Martin Engelcke, and Laurens van der Maaten · 2018
Cited alongside, same era.
Large-scale point cloud semantic segmentation with superpoint graphs
Loic Landrieu and Martin Simonovsky · 2018
Cited alongside, same era.
PointGrid: A deep network for 3D shape understanding
Dynamic graph cnn for learning on point clouds
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2019
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Learning object bounding boxes for 3D instance segmentation on point clouds
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, and Niki Trigoni · 2019
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ShellNet: Efficient point cloud convolutional neural networks using concentric shells statistics
Zhiyuan Zhang, Binh-Son Hua, and Sai-Kit Yeung · 2019
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DublinCity: Annotated lidar point cloud and its applications
SM Zolanvari, Susana Ruano, Aakanksha Rana, Alan Cummins, Rogerio Eduardo da Silva, Morteza Rahbar, and Aljosa Smolic · 2019
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SalsaNet: Fast road and vehicle segmentation in lidar point clouds for autonomous driving
Eren Erdal Aksoy, Saimir Baci, and Selcuk Cavdar · 2020
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Truc Le and Ye Duan · 2018
Cited alongside, same era.
PointCNN: Convolution on X-transformed points
Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen · 2018
Cited alongside, same era.
Paris-Lille-3D: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification
Xavier Roynard, Jean-Emmanuel Deschaud, and François Goulette · 2018
Cited alongside, same era.
Tangent convolutions for dense prediction in 3D
Maxim Tatarchenko, Jaesik Park, Vladlen Koltun, and Qian-Yi Zhou · 2018
Cited alongside, same era.
SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D lidar point cloud
Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer · 2018
Cited alongside, same era.
Pcn: Point completion network
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert · 2018
Cited alongside, same era.
A patch-based method for the evaluation of dense image matching quality
Zhenchao Zhang et al · 2018
Cited alongside, same era.
Architecting smart city digital twins: Combined semantic model and machine learning approach
Mark Austin, Parastoo Delgoshaei, Maria Coelho, and Mohammad Heidarinejad · 2020
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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
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Salsanext: Fast semantic segmentation of lidar point clouds for autonomous driving
Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy · 2020
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A2D2: Audi autonomous driving dataset
Jakob Geyer, Yohannes Kassahun, Mentar Mahmudi, Xavier Ricou, Rupesh Durgesh, Andrew S Chung, Lorenz Hauswald, Viet Hoang Pham, Maximilian Mühlegg, Sebastian Dorn, et al · 2020
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Deep learning for 3D point clouds: A survey
Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun · 2020
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RandLA-Net: Efficient semantic segmentation of large-scale point clouds
Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham · 2020
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Campus3d: A photogrammetry point cloud benchmark for hierarchical understanding of outdoor scene
Xinke Li, Chongshou Li, Zekun Tong, Andrew Lim, Junsong Yuan, Yuwei Wu, Jing Tang, and Raymond Huang · 2020
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Learning to segment 3D point clouds in 2D image space
Yecheng Lyu, Xinming Huang, and Ziming Zhang · 2020
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Semanticposs: A point cloud dataset with large quantity of dynamic instances
Yancheng Pan, Biao Gao, Jilin Mei, Sibo Geng, Chengkun Li, and Huijing Zhao · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al · 2020
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Toronto-3D: A large-scale mobile lidar dataset for semantic segmentation of urban roadways
Weikai Tan, Nannan Qin, Lingfei Ma, Ying Li, Jing Du, Guorong Cai, Ke Yang, and Jonathan Li · 2020
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CSPC-dataset: New lidar point cloud dataset and benchmark for large-scale scene semantic segmentation
Guofeng Tong, Yong Li, Dong Chen, Qi Sun, Wei Cao, and Guiqiu Xiang · 2020
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DALES: A large-scale aerial lidar data set for semantic segmentation
Nina Varney, Vijayan K Asari, and Quinn Graehling · 2020
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Pre-training by completing point clouds
Hanchen Wang, Qi Liu, Xiangyu Yue, Joan Lasenby, and Matthew J Kusner · 2020
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Pointcontrast: Unsupervised pre-training for 3d point cloud understanding
Saining Xie, Jiatao Gu, Demi Guo, Charles R Qi, Leonidas J Guibas, and Or Litany · 2020
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SqueezeSegV3: Spatially-adaptive convolution for efficient point-cloud segmentation
Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, and Masayoshi Tomizuka · 2020
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Lasdu: A large-scale aerial lidar dataset for semantic labeling in dense urban areas
Zhen Ye, Yusheng Xu, Rong Huang, Xiaohua Tong, Xin Li, Xiangfeng Liu, Kuifeng Luan, Ludwig Hoegner, and Uwe Stilla · 2020
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PolarNet: An improved grid representation for online lidar point clouds semantic segmentation
Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh · 2020
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