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3D点云数据集汇总

3D点云数据集汇总

分类数据集：

合成：
  1. Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3D shapeNets: A deep representation for volumetric shapes,” in CVPR, 2015.
  2. A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, and H. Su, “ShapeNet: An information-rich 3D model repository,” arXiv preprint arXiv:1512.03012, 2015.
真实：
  1. M. A. Uy, Q.-H. Pham, B.-S. Hua, T. Nguyen, and S.-K. Yeung, “Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,” in ICCV, 2019.
  2. A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “ScanNet: Richly-annotated 3D reconstructions of indoor scenes,” in CVPR, 2017.

目标检测数据集：

室内：
  1. A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “ScanNet: Richly-annotated 3D reconstructions of indoor scenes,” in CVPR, 2017.
  2. S. Song, S. P. Lichtenberg, and J. Xiao, “Sun RGB-D: A RGB-D scene understanding benchmark suite,” in CVPR, 2015.
室外：
  1. A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving,” in CVPR, 2012.
  2. R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska et al., “Lyft level 5 av dataset 2019,” 2019.
  3. P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in perception for autonomous driving: Waymo open dataset,” in CVPR, 2020.
  4. H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in CVPR, 2020.

3D点云分割：

Mobile Laser Scanners (MLS，移动激光扫描仪):
  1. J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “SemanticKITTI: A dataset for semantic scene understanding of lidar sequences,” in ICCV, 2019.
  2. A. Serna, B. Marcotegui, F. Goulette, and J.-E. Deschaud, “Parisrue-madame database: a 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods,” in ICRA, 2014.
  3. X. Roynard, J.-E. Deschaud, and F. Goulette, “Paris-lille-3d: A large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification,” IJRR, 2018.
Aerial Laser Scanners (ALS，航空激光扫描仪):
  1. F. Rottensteiner, G. Sohn, J. Jung, M. Gerke, C. Baillard, S. Benitez, and U. Breitkopf, “The isprs benchmark on urban object classification and 3D building reconstruction,” ISPRS, 2012.
  2. N. Varney, V. K. Asari, and Q. Graehling, “Dales: A large-scale aerial lidar data set for semantic segmentation,” arXiv preprint arXiv:2004.11985, 2020.
Terrestrial Laser Scanners (TLS，地面激光扫描仪):

T. Hackel, N. Savinov, L. Ladicky, J. Wegner, K. Schindler, and M. Pollefeys, “Semantic3D.net: A new large-scale point cloud classification benchmark,” ISPRS, 2017.

RGB-D cameras:

A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “ScanNet: Richly-annotated 3D reconstructions of indoor scenes,” in CVPR, 2017.

3D scanners:

I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis, M. Fischer, and S. Savarese, “3D semantic parsing of large-scale indoor spaces,” in CVPR, 2016.