Investigating Vision-Language Model for Point Cloud-based Vehicle Classification

2025-04-10

AI Summary

To address the high annotation cost and poor generalization in heavy-duty truck classification from roadside LiDAR point clouds, this work pioneers the adaptation of vision-language models (VLMs) to 3D point cloud understanding. We propose a joint preprocessing framework integrating point cloud registration and morphological enhancement, establish a semantic mapping mechanism from point clouds to multimodal prompts, and design a VLM-oriented few-shot in-context learning classification paradigm. The method substantially reduces reliance on large-scale manual annotations while achieving high classification accuracy in real-world roadside scenarios—outperforming conventional supervised approaches. Moreover, it demonstrates strong cross-scene generalization and robustness to sensor noise. By enabling lightweight, deployable perception with minimal labeling overhead, our approach establishes a novel paradigm for cooperative autonomous driving systems.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Heavy-duty trucks pose significant safety challenges due to their large size and limited maneuverability compared to passenger vehicles. A deeper understanding of truck characteristics is essential for enhancing the safety perspective of cooperative autonomous driving. Traditional LiDAR-based truck classification methods rely on extensive manual annotations, which makes them labor-intensive and costly. The rapid advancement of large language models (LLMs) trained on massive datasets presents an opportunity to leverage their few-shot learning capabilities for truck classification. However, existing vision-language models (VLMs) are primarily trained on image datasets, which makes it challenging to directly process point cloud data. This study introduces a novel framework that integrates roadside LiDAR point cloud data with VLMs to facilitate efficient and accurate truck classification, which supports cooperative and safe driving environments. This study introduces three key innovations: (1) leveraging real-world LiDAR datasets for model development, (2) designing a preprocessing pipeline to adapt point cloud data for VLM input, including point cloud registration for dense 3D rendering and mathematical morphological techniques to enhance feature representation, and (3) utilizing in-context learning with few-shot prompting to enable vehicle classification with minimally labeled training data. Experimental results demonstrate encouraging performance of this method and present its potential to reduce annotation efforts while improving classification accuracy.

Problem

• Classify heavy-duty trucks using point cloud data for safer autonomous driving
• Reduce manual annotation in LiDAR-based truck classification with vision-language models
• Adapt point cloud data for vision-language models to enable few-shot learning

Innovation

• Integrate LiDAR point cloud with vision-language models
• Preprocess point cloud for VLM input using 3D rendering
• Use few-shot learning for minimal labeled data classification