RefDiffMap: Diffusion-Guided Progressive Refinement for Vectorized HD Map Construction

Abstract

High-definition (HD) map learning serves as an essential component of autonomous driving scene understanding, providing structured priors for planning and prediction. Recent transformer-based methods regress vectorized map elements via deformable attention over Bird's-Eye View (BEV) features. They typically employ a single-pass paradigm, starting from a set of initial queries. However, these queries struggle to precisely localize map elements within the large-scale BEV space. This difficulty is severely amplified when using lightweight backbones that produce less distinctive features. To address this, we propose RefDiffMap, which recasts map construction as a progressive refinement process driven by a diffusion model. We introduce a novel denoising query generator that, at each step, leverages the intermediate noisy geometry to sample relevant features from adaptive BEV RoIs. These features are distilled into context-aware queries that guide the decoder's next refinement. This creates a powerful geometry-feature co-evolution loop, allowing the model to iteratively correct localization errors. Comprehensive experiments show that RefDiffMap achieves competitive performance on the nuScenes and Argoverse 2 datasets. Notably, its robustness is highlighted with a ResNet-18 backbone, where it improves mAP by a significant 11.3% over our baseline MapTRv2. Further ablation studies validate the effectiveness of our approach.