Integration of HD Maps and Point Clouds: An Efficient 3D Reconstruction Framework for Autonomous Driving Applications

Kurzfassung

Autonomous driving approaches require simulation environments that accurately converge real-world conditions. We can utilize 3D reconstruction methodologies to achieve this. In this paper, we propose a lightweight 3D reconstruction methodology by using the Geospatial Data Abstraction Library (GDAL) and existing HD maps in the OpenDRIVE data format. By leveraging these datasets, we aim to improve the efficiency and accessibility of 3D scene reconstruction for autonomous driving applications. Additionally, we aim to provide a low-cost solution to address the annotation bottleneck in point-wise labeling for the computer vision domain with the constructed 3D models.