OpenTwinMap: An Open-Source Digital Twin Generator for Urban Autonomous Driving

arXiv 25.11 2025 40.8 system, application

TLDR

OpenTwinMap is an open-source Python framework for generating 3D urban digital twins from LiDAR and OSM data for autonomous driving simulation.

Reasoning

The paper presents a practical, extensible tool for creating digital twins, addressing limitations of existing tightly-coupled solutions. However, the framework is still in early stages with only basic mesh and terrain generation, and lacks evaluation on diverse urban contexts or integration with world models.

Read-first score

Read-first score 40.8, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 6.

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=evaluation,experiment,result,validation

Recency 8%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 42%

8.6

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Field roles

FrontierMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 399.

Keyword Scores

world simulator

world model

generative world model

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Deep Analysis

Innovations

Open-source Python-based framework for generating high-fidelity 3D urban digital twins
Ingests LiDAR scans and OpenStreetMap (OSM) data to produce semantically segmented static environment assets
Emphasizes extensibility and parallelization to lower the barrier for researchers to adapt and scale the pipeline
Exports generated assets to Unreal Engine for autonomous vehicle simulation
Provides basic road mesh and terrain generation with preliminary CARLA integration

Methodology

OpenTwinMap is a Python-based framework that preprocesses LiDAR scans and OpenStreetMap data to generate semantically segmented static environment assets, including road networks, terrain, and urban structures. The pipeline is designed for extensibility and parallelization, and the output can be exported into Unreal Engine for AV simulation. The current implementation includes preprocessing, basic road mesh and terrain generation, and preliminary support for CARLA integration.

Key Results

The paper describes the current capabilities of OpenTwinMap, which include preprocessing of OSM and LiDAR data, basic road mesh and terrain generation, and preliminary CARLA integration. No quantitative experimental results or evaluation are reported.

Limitations

The framework is not yet complete; only basic road mesh and terrain generation are implemented
CARLA integration is preliminary and may not be fully functional
Only static environment assets are generated; dynamic objects are not included
The paper does not present experimental validation or quantitative results
Reliance on LiDAR and OSM data may limit applicability to areas where such data are unavailable or of low quality

Resources

Paper