PANDORA: A Panoramic Detection Dataset for Object with Orientation

Hang Xu, Qiang Zhao, Yike Ma, Xiaodong Li, Peng Yuan, Bailan Feng, Chenggang Yan, Feng Dai ;

"Panoramic images have become increasingly popular as omnidirectional panoramic technology has advanced. Many datasets and works resort to object detection to better understand the content of the panoramic image. These datasets and detectors use a Bounding Field of View (BFoV) as a bounding box in panoramic images. However, we observe that the object instances in panoramic images often appear with arbitrary orientations. It indicates that BFoV as a bounding box is inappropriate, limiting the performance of detectors. This paper proposes a new bounding box representation, Rotated Bounding Field of View (RBFoV), for the panoramic image object detection task. Then, based on the RBFoV, we present a PANoramic Detection dataset for Object with oRientAtion (PANDORA). Finally, based on PANDORA, we evaluate the current state-of-the-art panoramic image object detection methods and design an anchor-free object detector called R-CenterNet for panoramic images. Compared with these baselines, our R-CenterNet shows its advantages in terms of detection performance. Our PANDORA dataset and source code are available at https://github.com/tdsuper/SphericalObjectDetection."