  360{\deg} images are usually represented in either equirectangular projection
(ERP) or multiple perspective projections. Different from the flat 2D images,
the detection task is challenging for 360{\deg} images due to the distortion of
ERP and the inefficiency of perspective projections. However, existing methods
mostly focus on one of the above representations instead of both, leading to
limited detection performance. Moreover, the lack of appropriate bounding-box
annotations as well as the annotated datasets further increases the
difficulties of the detection task. In this paper, we present a standard object
detection framework for 360{\deg} images. Specifically, we adapt the
terminologies of the traditional object detection task to the omnidirectional
scenarios, and propose a novel two-stage object detector, i.e., Reprojection
R-CNN by combining both ERP and perspective projection. Owing to the
omnidirectional field-of-view of ERP, Reprojection R-CNN first generates coarse
region proposals efficiently by a distortion-aware spherical region proposal
network. Then, it leverages the distortion-free perspective projection and
refines the proposed regions by a novel reprojection network. We construct two
novel synthetic datasets for training and evaluation. Experiments reveal that
Reprojection R-CNN outperforms the previous state-of-the-art methods on the mAP
metric. In addition, the proposed detector could run at 178ms per image in the
panoramic datasets, which implies its practicability in real-world
applications.
