Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues
This addresses limitations in autonomous driving map vector estimation, offering a novel method for improved accuracy, though it appears incremental as it builds on existing pipelines.
The paper tackles the problem of inaccurate shape restoration and missing instances in vectorized HD map construction from surround-view cameras by proposing PerCMap, which uses perspective-view features at instance and point levels, achieving 67.1 and 70.5 mAP on nuScenes and Argoverse 2 benchmarks.
The construction of vectorized High-Definition (HD) maps from onboard surround-view cameras has become a significant focus in autonomous driving. However, current map vector estimation pipelines face two key limitations: input-agnostic queries struggle to capture complex map structures, and the view transformation leads to information loss. These issues often result in inaccurate shape restoration or missing instances in map predictions. To address this concern, we propose a novel approach, namely \textbf{PerCMap}, which explicitly exploits clues from perspective-view features at both instance and point level. Specifically, at instance level, we propose Cross-view Instance Activation (CIA) to activate instance queries across surround-view images, thereby helping the model recover the instance attributes of map vectors. At point level, we design Dual-view Point Embedding (DPE), which fuses features from both views to generate input-aware positional embeddings and improve the accuracy of point coordinate estimation. Extensive experiments on \textit{nuScenes} and \textit{Argoverse 2} demonstrate that PerCMap achieves strong and consistent performance across benchmarks, reaching 67.1 and 70.5 mAP, respectively.