Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues

• URL: http://arxiv.org/abs/2404.11155v2
• Date: Sat, 11 Oct 2025 15:29:29 GMT
• Title: Improving Hierarchical Representations of Vectorized HD Maps with Perspective Clues
• Authors: Chi Zhang, Qi Song, Feifei Li, Jie Li, Rui Huang,
• Abstract summary: We propose PerCMap, which exploits clues from perspective-view features at both instance and point level.<n>PerCMap achieves strong and consistent performance across benchmarks, reaching 67.1 and 70.5 mAP, respectively.
• Score: 20.730599565199935
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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