Abstract
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.
H. Xu and Q. Zhao—This work was done when Hang Xu and Qiang Zhao were at ICT.
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Acknowledgements
This work is supported by the National Key Research and Development Program of China (2020YFB1406604) and the National Natural Science Foundation of China (62072438, U1936110, 61931008, U21B2024).
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Xu, H. et al. (2022). PANDORA: A Panoramic Detection Dataset for Object with Orientation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_14
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