Learning from THEODORE

News

• 2020-03-14 - FES (v1.0) dataset released with images, bounding boxes and masks
• 2020-03-14 - THEODORE (v1.0) dataset released with images, bounding boxes and masks

Abstract

Recent work about synthetic indoor datasets from perspective views has shown significant improvements of object detection results with Convolutional Neural Networks(CNNs). In this paper, we introduce THEODORE: a novel, large-scale indoor dataset containing 100,000 high-resolution diversified fisheye images with 14 classes. To this end, we create 3D virtual environments of living rooms, different human characters and interior textures. Beside capturing fisheye images from virtual environments we create annotations for semantic segmentation, instance masks and bounding boxes for object detection tasks. We compare our synthetic dataset to state of the art real-world datasets for omnidirectional images. Based on MS COCO weights, we show that our dataset is well suited for fine-tuning CNNs for object detection. Through a high generalization of our models by means of image synthesis and domain randomization we reach an AP up to 0.84 for class person on High-Definition Analytics dataset.

Paper

• WACV 2020
• arXiv
• Data
• Presentation

BibTeX

If you use the data set in your work, please don't forget to cite:

@inproceedings{scheck2020theodore,
  title={Learning from THEODORE: A Synthetic Omnidirectional Top-View Indoor Dataset for Deep Transfer Learning},
  author={Tobias Scheck and Roman Seidel and Gangolf Hirtz},
  booktitle={2020 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2020}
}

License

This data set is licensed under a Creative Commons Attribution 4.0 International License.