Learning from THEODORE

News

• 2020-03-14 - FES (v1.0) Datensatz mit Bildern, Begrenzungsrahmen und Masken veröffentlicht
• 2020-03-14 - THEODORE (v1.0) Datensatz mit Bildern, Begrenzungsrahmen und Masken veröffentlicht

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
• Daten
• Präsentation

BibTeX

Wenn Sie den Datensatz in Ihrer Arbeit verwenden, bitte folgende Referenz nicht vergessen:

@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}
}

Lizenz

Der Datensatz ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz.