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In the era of big data and large models, automatic annotating functions formulti-modal data are of great significance for real-world AI-drivenapplications, such as autonomous driving and embodied AI. Unlike traditionalclosed-set annotation, open-vocabulary annotation is essential to achievehuman-level cognition capability. However, there are few open-vocabularyauto-labeling systems for multi-modal 3D data. In this paper, we introduceOpenAnnotate3D, an open-source open-vocabulary auto-labeling system that canautomatically generate 2D masks, 3D masks, and 3D bounding box annotations forvision and point cloud data. Our system integrates the chain-of-thoughtcapabilities of Large Language Models (LLMs) and the cross-modalitycapabilities of vision-language models (VLMs). To the best of our knowledge,OpenAnnotate3D is one of the pioneering works for open-vocabulary multi-modal3D auto-labeling. We conduct comprehensive evaluations on both public andin-house real-world datasets, which demonstrate that the system significantlyimproves annotation efficiency compared to manual annotation while providingaccurate open-vocabulary auto-annotating results.

OpenAnnotate3D: Open-Vocabulary Auto-Labeling System for Multi-modal 3D Data