论文标题
从单个鱼眼图像中检测3D对象检测，而没有单个鱼眼训练图像
3D Object Detection from a Single Fisheye Image Without a Single Fisheye Training Image
论文作者
论文摘要
现有的单眼3D对象检测方法已在直线透视图中进行了证明，并且在带有替代投影（例如Fisheye摄影机获取的图像）中失败。 Fisheye图像中有关对象检测的先前工作集中在2D对象检测上，部分原因是缺乏此类图像的3D数据集。在这项工作中，我们展示了如何使用仅在直线图像上训练的现有单眼3D对象检测模型，以检测来自Fisheye摄像机的图像中的3D对象，而无需使用任何Fisheye训练数据。尽管事实上现有方法在目标非线性投影上训练了综合数据的基准，但我们的表现胜过全景图中唯一现有的单眼3D对象检测方法，而我们仅在直线图像上训练。我们还尝试了一个真实鱼眼图像的内部数据集。
Existing monocular 3D object detection methods have been demonstrated on rectilinear perspective images and fail in images with alternative projections such as those acquired by fisheye cameras. Previous works on object detection in fisheye images have focused on 2D object detection, partly due to the lack of 3D datasets of such images. In this work, we show how to use existing monocular 3D object detection models, trained only on rectilinear images, to detect 3D objects in images from fisheye cameras, without using any fisheye training data. We outperform the only existing method for monocular 3D object detection in panoramas on a benchmark of synthetic data, despite the fact that the existing method trains on the target non-rectilinear projection whereas we train only on rectilinear images. We also experiment with an internal dataset of real fisheye images.
