Birds-Eye-View (BEV) maps have emerged as one of the most powerful representations for scene understanding due to their ability to provide rich spatial context while being easy to interpret and process. However, generating BEV maps requires complex multi-stage paradigms that encapsulate a series of distinct tasks such as depth estimation, ground plane estimation, and semantic segmentation. These sub-tasks are often learned in a disjoint manner which prevents the model from holistic reasoning and results in erroneous BEV maps. Moreover, existing algorithms only predict the semantics in the BEV space, which limits their use in applications where the notion of object instances is critical. In this work, we present the first end-to-end learning approach for directly predicting dense panoptic segmentation maps in the BEV, given a single monocular image in the frontal view (FV). Our architecture follows the top-down paradigm and incorporates a novel dense transformer module consisting of two distinct transformers that learn to independently map vertical and flat regions in the input image from the FV to the BEV. Additionally, we derive a mathematical formulation for the sensitivity of the FV-BEV transformation which allows us to intelligently weight pixels in the BEV space to account for the varying descriptiveness across the FV image. Extensive evaluations on the KITTI-360 and nuScenes datasets demonstrate that our approach exceeds the state-of-the-art in the PQ metric by 3.61 pp and 4.93 pp respectively.
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