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arXiv · 2605.05897

Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis

Yuhan Xia·Runxin Zhao·Hanyang Zhuang·Chunxiang Wang·Ming Yang
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Abstract

Intelligent Transportation Systems (ITS) require reliable environmental perception to support safe and efficient transportation. With the rapid development of Vehicle-to-everything (V2X), roadside perception has become an effective means to extend sensing coverage and improve traffic safety. However, the scarcity of large-scale annotated roadside LiDAR datasets poses a major challenge for training high-performance roadside perception models. In this paper, we introduce Vehicle-to-Roadside LiDAR Synthesis (VRS), a data synthesis framework that generates labeled roadside LiDAR datasets from vehicle-side datasets via LiDAR novel view synthesis. To mitigate the vehicle-to-roadside domain gap, VRS employs vehicle point cloud completion to compensate for missing geometry in vehicle-side observations, and introduces an occupancy-based visibility constraint to handle large viewpoint changes during cross-view rendering. The proposed framework enables flexible multi-view rendering for scalable roadside data generation. Extensive experiments on roadside 3D object detection demonstrate that the synthesized data effectively complements real roadside data, mitigates the limitations of limited real-world roadside data, and improves generalization to unseen roadside viewpoints.

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Yuhan Xia, Runxin Zhao, Hanyang Zhuang, Chunxiang Wang, Ming Yang. 2026-05-07. Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis. https://arxiv.org/abs/2605.05897

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