GaussianMap: Learning Gaussian Representation for Multi-Sensor Online HD Map Construction
Authors: Hongyu Lyu, Julie Stephany Berrio Perez, Mao Shan, Stewart Worrall
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.
Accurate High-Definition (HD) map construction is critical for autonomous driving, yet existing methods face a fundamental trade-off: vectorization-based approaches preserve topology but struggle with geometric fidelity, while rasterization-based approaches enable precise geometric supervision but produce unstructured outputs. To bridge this gap, we propose GSMap, a novel framework that unifies both paradigms via a learnable 2D Gaussian representation. Each map element is modeled as an ordered sequence of 2D Gaussians, whose centers correspond to the vertices of the vectorized polyline/polygon. This formulation enables simultaneous optimization through: (1) Differentiable rasterization that enforces pixel-level geometric constraints, and (2) Topology-aware vectorization that maintains structural regularity. Experiments on both nuScenes and Argoverse2 demonstrate that our Gaussian-based representation effectively unifies geometric and topological learning, achieving significant performance improvements and demonstrating strong compatibility with existing HD mapping architectures. Code will be available at https://github.com/peakpang/GSMap
High-definition (HD) maps are essential for autonomous driving systems. In constructing such maps, onboard multi-view camera images, standard-definition maps and satellite images provide crucial information. However, due to the modality and perspective differences among these data sources, existing methods often struggle to effectively align and fuse them, making online HD map construction still challenging. To address these issues, we propose Driver2Map, an online HD map construction model inspired by human drivers. Unlike existing HD map construction models that utilize only two modalities, our Driver2Map can simultaneously exploit three modalities. Specifically, we propose a "two-stage alignment" strategy to reduce spatial misalignment across different modalities. Additionally, we introduce "Pose-Guided BEV Fusion", a BEV (bird's-eye-view) generation module that leverages camera pose information to adaptively weight multi-view features, thereby effectively suppressing cross-view feature overlap during BEV generation. Also, we design a "Pretrained Prior for Map Refinement" module to refine the initial prediction by learning map structure priors, thus improving the HD map prediction under dynamic occlusions. Extensive experiments demonstrate that Driver2Map outperforms existing methods on both IoU and AP metrics.
Offline high-definition maps provide essential geometric and topological priors for autonomous driving systems. Pure-vision solutions have become the predominant paradigm for offline mapping due to their cost-effectiveness and scalability. However, local-to-global mapping under visual conditions confronts two fundamental challenges: single-shot local observations are susceptible to viewpoint variation and environmental interference, leading to geometric deviations, while multi-source local information exhibits heterogeneous confidence, rendering globally consistent aggregation difficult. To address these, this paper proposes L2G-Map, a framework comprising hierarchical prior diffusion refinement and elliptical space Bayesian fusion. The former jointly embeds temporal context and centerline priors to guide structure completion and topology recovery during denoising, alleviating the information incompleteness inherent in pure-vision settings. The latter incorporates an adaptive weighting strategy driven by elliptical distance propagation, enabling probabilistically optimal aggregation of multi-source information under the Bayesian posterior update paradigm. Extensive experiments on nuScenes and Argoverse benchmark datasets verify the effectiveness of L2G-Map. The proposed refinement component yields consistent local map accuracy improvements across different datasets. Under sensor-degraded conditions, a 3.27% mIoU gain is achieved. Furthermore, the adaptive fusion component significantly enhances the accuracy of global maps. The fused global map can be flexibly embedded into different online map models, yielding an 18.26% mIoU improvement in semantic map construction and a 20.00% enhancement in vectorized map construction, demonstrating the overall advantages of the proposed closed-loop pipeline. Source code will be available at https://github.com/lynn-yu/L2G-Map.