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.

本文介绍一个新的大规模室内合成数据集THEODORE，包含100,000张物体的高分辨率多样化鱼眼图像，附带语义分割、实例掩模和物体检测任务的边界框注释，通过图像合成和域随机化高度泛化我们的模型，AP可达0.84

从THEODORE学习：用于深度迁移学习的合成全景室内顶视数据集