MapDiffusion uses a diffusion decoder conditioned on a latent BEV grid to sample multiple vectorized HD maps, reporting a 5.3% relative mAP gain over StreamMapNet on nuScenes and uncertainty that increases in occluded areas.
TempBEV: Improving learned bev encoders with combined image and bev space temporal aggregation,
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MapDiffusion: Generative Diffusion for Vectorized Online HD Map Construction and Uncertainty Estimation in Autonomous Driving
MapDiffusion uses a diffusion decoder conditioned on a latent BEV grid to sample multiple vectorized HD maps, reporting a 5.3% relative mAP gain over StreamMapNet on nuScenes and uncertainty that increases in occluded areas.