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

MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models