GaussianMap: Learning Gaussian Representation for Multi-Sensor Online HD Map Construction

2026-06-30

AI Summary

This work addresses the limitations of existing online high-definition map construction methods, which rely on fixed-resolution bird’s-eye-view (BEV) grids and struggle to efficiently model sparse yet geometrically precise map elements. To overcome this, the authors propose an online mapping framework based on adaptive Gaussian representations, replacing conventional dense BEV grids with learnable Gaussian primitives that enable dynamic allocation of modeling resources and high-fidelity geometric representation in critical regions. The approach leverages Gaussian interaction modeling and multi-sensor feature fusion to generate structured BEV features through a feedforward encoder, which are subsequently decoded into vectorized maps. Evaluated on the nuScenes and Argoverse 2 datasets, the method achieves state-of-the-art performance under both pure vision and camera–LiDAR fusion settings.

Abstract

Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online construction of HD maps offers a scalable approach to generate local vectorized maps from onboard sensor observations. Existing methods commonly adopt bird's-eye-view (BEV) features as the intermediate scene representation, encoding the surrounding space with fixed-resolution dense grids. However, map elements are spatially sparse yet require fine-grained geometric localization, making uniformly allocated BEV representations redundant and less effective for vectorized map prediction. In this work, we propose GaussianMap, an online HD map construction framework that learns an adaptive Gaussian representation of the surrounding scene. This representation consists of a set of Gaussian primitives on the BEV plane, each encoding a flexible local region with geometric properties and a feature vector, allowing the model to allocate representational capacity to map-relevant regions. To generate such a representation from sensor observations, we introduce a feed-forward Gaussian encoder that progressively refines these primitives through Gaussian interaction modeling and multi-sensor feature aggregation. The refined Gaussian representation is then splatted into a BEV feature map and decoded into vectorized map predictions. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that GaussianMap achieves state-of-the-art performance in both camera-only and camera-LiDAR fusion settings. Our code will be made publicly available.

Problem

• HD map construction
• bird's-eye-view representation
• vectorized map prediction
• spatial sparsity
• geometric localization

Innovation

• Gaussian representation
• online HD map construction
• adaptive scene representation
• vectorized map prediction
• multi-sensor fusion