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

• URL: http://arxiv.org/abs/2605.05897v1
• Date: Thu, 07 May 2026 09:10:18 GMT
• Title: Generating Roadside LiDAR Datasets from Vehicle-Side Datasets via Novel View Synthesis
• Abstract summary: We introduce Vehicle-to-Roadside LiDAR Synthesis (VRS), a data synthesis framework that generates labeled roadside LiDAR datasets from vehicle-side datasets via novel view synthesis.<n>To mitigate the vehicle-to-roadside domain gap, VRS employs vehicle point cloud completion to compensate for missing geometry in vehicle-side observations.<n>The proposed framework enables flexible multi-view rendering for scalable roadside data generation.
• Score: 14.327452890872188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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