Six-to-one: Cubemap-guided Feature Calibration for Panorama Object Detection

Object detection methods for perspective images have proven increasingly efficient, but the techniques for equirectangular projection (ERP) panoramas from inherently spherical imaging cannot still achieve satisfactory performance. Due to the various degrees of distortion at different pixel locations, current algorithms cannot adapt to the changes in shape and contour caused by stretching, which results in performance degradation when migrating them from perspective images to spherical ones. In this paper, we improve the network for panorama object detection and introduce the cube-domain information with discontinuity but low distortion to correct the panorama features. Unlike previous works, we consider the impact of semantic discontinuity from all tangent planes instead of overlaying features when needed. Considering the six facets as unified, i.e., six-to-one for extraction, the proposed Facet-Link module enhances the long-range sensing capability at the facet level in the frequency domain. Moreover, the position alignment packs different facets, i.e., six-to-one for calibration, to preserve more global signals during the correction stage, which establishes semantic pathways for feature interactions between panorama and cubemap in the two dimensions, facet-facet and cube-pano, respectively. Extensive experiments on synthetic and real-world datasets verify the effectiveness and robustness of our proposed method.