  While there are several widely used object detection datasets, current
computer vision algorithms are still limited in conventional images. Such
images narrow our vision in a restricted region. On the other hand, 360{\deg}
images provide a thorough sight. In this paper, our goal is to provide a
standard dataset to facilitate the vision and machine learning communities in
360{\deg} domain. To facilitate the research, we present a real-world 360{\deg}
panoramic object detection dataset, 360-Indoor, which is a new benchmark for
visual object detection and class recognition in 360{\deg} indoor images. It is
achieved by gathering images of complex indoor scenes containing common objects
and the intensive annotated bounding field-of-view. In addition, 360-Indoor has
several distinct properties: (1) the largest category number (37 labels in
total). (2) the most complete annotations on average (27 bounding boxes per
image). The selected 37 objects are all common in indoor scene. With around 3k
images and 90k labels in total, 360-Indoor achieves the largest dataset for
detection in 360{\deg} images. In the end, extensive experiments on the
state-of-the-art methods for both classification and detection are provided. We
will release this dataset in the near future.
