Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues

TL;DR

PerCMap enhances vectorized HD map construction from surround-view images by leveraging perspective clues through cross-view instance activation and dual-view point embedding, significantly improving accuracy and consistency in autonomous driving applications.

Contribution

The paper introduces PerCMap, a novel method that explicitly exploits perspective-view clues at both instance and point levels to improve map vector estimation.

Findings

Achieves 67.1 mAP on nuScenes

Achieves 70.5 mAP on Argoverse 2

Demonstrates strong, consistent performance across benchmarks

Abstract

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.