  Despite tremendous advancements in bird's-eye view (BEV) perception, existing
models fall short in generating realistic and coherent semantic map layouts,
and they fail to account for uncertainties arising from partial sensor
information (such as occlusion or limited coverage). In this work, we introduce
MapPrior, a novel BEV perception framework that combines a traditional
discriminative BEV perception model with a learned generative model for
semantic map layouts. Our MapPrior delivers predictions with better accuracy,
realism, and uncertainty awareness. We evaluate our model on the large-scale
nuScenes benchmark. At the time of submission, MapPrior outperforms the
strongest competing method, with significantly improved MMD and ECE scores in
camera- and LiDAR-based BEV perception.

Computer Vision and Pattern Recognition;Robotics