  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.
