SafeMap: Robust HD Map Construction from Incomplete Observations

2025-07-01

AI Summary

To address the insufficient robustness of high-definition (HD) map construction under incomplete multi-view camera data, this paper proposes SafeMap—a novel end-to-end framework. First, it introduces a Gaussian-distribution-based dynamic view reconstruction module that leverages viewpoint importance priors to achieve geometrically consistent reconstruction of missing views. Second, it incorporates a distillation-driven bird’s-eye-view (BEV) correction module, which enhances map accuracy via panoramic feature distillation and BEV-space feature alignment. The framework jointly integrates multi-view geometry, Gaussian modeling, knowledge distillation, and dynamic region prioritization. Extensive experiments demonstrate that SafeMap consistently outperforms state-of-the-art methods under both complete and incomplete observations, achieving superior accuracy and robustness in HD map generation for autonomous driving. Its modular design ensures plug-and-play compatibility with existing perception pipelines.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Robust high-definition (HD) map construction is vital for autonomous driving, yet existing methods often struggle with incomplete multi-view camera data. This paper presents SafeMap, a novel framework specifically designed to secure accuracy even when certain camera views are missing. SafeMap integrates two key components: the Gaussian-based Perspective View Reconstruction (G-PVR) module and the Distillation-based Bird's-Eye-View (BEV) Correction (D-BEVC) module. G-PVR leverages prior knowledge of view importance to dynamically prioritize the most informative regions based on the relationships among available camera views. Furthermore, D-BEVC utilizes panoramic BEV features to correct the BEV representations derived from incomplete observations. Together, these components facilitate the end-to-end map reconstruction and robust HD map generation. SafeMap is easy to implement and integrates seamlessly into existing systems, offering a plug-and-play solution for enhanced robustness. Experimental results demonstrate that SafeMap significantly outperforms previous methods in both complete and incomplete scenarios, highlighting its superior performance and reliability.

Problem

• Robust HD map construction from incomplete camera views
• Dynamic prioritization of informative regions with missing data
• Correcting BEV representations using panoramic features

Innovation

• Gaussian-based Perspective View Reconstruction module
• Distillation-based Bird's-Eye-View Correction module
• End-to-end robust HD map generation