Methodology for Scalable LiDAR Datasets

The use of Light Detection and Ranging (LiDAR) in intelligent transportation systems has gained increasing attention due to its ability to provide accurate three-dimensional representations of the environment and its robustness under adverse weather conditions compared to camera based systems. However, traditional classification approaches, whether based on geometric features or deep learning, are highly dependent on sensor configuration and can suffer from occlusions or incomplete representations. This work proposes a classification and retrieval based methodology leveraging embedding vectors in a latent space generated through PointNet and a KNN based label assignment strategy. A novel auto-labeling process is introduced, incorporating ID consistency filtering to ensure coherent label propagation across multiple captures of the same vehicle. Additionally, a combined proximity measure is used in KNN retrieval, integrating both latent space similarity and sensor distance correction, enhancing classification robustness, particularly for underrepresented categories. The proposed approach is validated on real world LiDAR data captured from fixed infrastructure, addressing the scarcity of publicly available datasets in this configuration. Results demonstrate that this method provides an accurate, scalable, and sensor-independent classification framework, ensuring reliable label assignment across diverse traffic conditions.