@InProceedings{toshilombo:siggraph:2018,
  author    = {Toshilombo, Kalenga-Bimpambu and Yoshiyasu, Yusuke and Gabas, Antonio and Suzui, Kota},
  title     = {Automatic dataset generation for object pose estimation},
  booktitle = {SIGGRAPH},
  year      = {2018},
  pages     = {01--02},
  address   = {Vancouver, Canada},
  month     = {August 12-August 16},
  url       = {https://www.researchgate.net/publication/329334784\_Automatic\_dataset\_generation\_for\_object\_pose\_estimation},
  keywords  = {Structure from Motion, CNN, pose recognition, Automatic dataset construction},
  doi       = {https://doi.org/10.1145/3283289.3283326},
  abstract  = {Object pose estimation based on a RGB image is essential in accomplishing many computer vision tasks, such as augmented reality and robot vision for grasping. Using structure from motion and domain randomization, we propose a method that, from a set of images, allows us to quickly generate large datasets to train a Convolutional Neural Network (ConvNet) for object pose estimation.}
}