Bird’s-Eye-View Panoptic Segmentation Using Monocular Frontal View Images
نویسندگان
چکیده
Bird’s-Eye-View (BEV) maps have emerged as one of the most powerful representations for scene understanding due to their ability provide rich spatial context while being easy interpret and process. Such found use in many real-world tasks that extensively rely on accurate segmentation well object instance identification BEV space operation. However, existing algorithms only predict semantics space, which limits applications where notion instances is also critical. In this work, we present first panoptic approach directly predicting dense BEV, given a single monocular image frontal view (FV). Our architecture follows top-down paradigm incorporates novel transformer module consisting two distinct transformers learn independently map vertical flat regions input from FV BEV. Additionally, derive mathematical formulation sensitivity FV-BEV transformation allows us intelligently weight pixels account varying descriptiveness across image. Extensive evaluations KITTI-360 nuScenes datasets demonstrate our exceeds state-of-the-art PQ metric by 3.61 pp 4.93 respectively.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2022
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2022.3142418