Point Cloud as a Foreign Language for Multi-modal Large Language Model

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• SAGE is the first end-to-end 3D MLLM that directly processes raw point clouds without relying on a pre-trained 3D encoder.technical 95%
• SAGE uses a lightweight 3D tokenizer that combines geometric sampling and neighbourhood aggregation with vector quantization to convert point clouds into discrete tokens.method 90%
• SAGE treats 3D data as a foreign language that naturally extends the LLM's vocabulary.method 85%
• A preference optimization training strategy with a semantic alignment-based reward is proposed to enhance the model's reasoning capability on complex 3D tasks.method 90%
• SAGE outperforms existing encoder-based methods on diverse 3D understanding benchmarks.result 90%
• SAGE offers significant advantages in computational efficiency compared to encoder-based methods.result 85%
• SAGE demonstrates better generalization across LLM backbones compared to encoder-based methods.result 80%
• SAGE exhibits greater robustness to input resolution variations compared to encoder-based methods.result 80%

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