Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning

• URL: http://arxiv.org/abs/2410.00255v1
• Date: Mon, 30 Sep 2024 21:55:38 GMT
• Title: Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
• Authors: Weitai Kang, Haifeng Huang, Yuzhang Shang, Mubarak Shah, Yan Yan,
• Abstract summary: We introduce Robin3D, a powerful 3DLLM trained on large-scale instruction-following data. We construct 1 million instruction-following data, consisting of 344K Adversarial samples, 508K Diverse samples, and 165K benchmark training set samples. Robin3D consistently outperforms previous methods across five widely-used 3D multimodal learning benchmarks.
• Score: 55.339257446600634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in 3D Large Language Models (3DLLMs) have highlighted their potential in building general-purpose agents in the 3D real world, yet challenges remain due to the lack of high-quality robust instruction-following data, leading to limited discriminative power and generalization of 3DLLMs. In this paper, we introduce Robin3D, a powerful 3DLLM trained on large-scale instruction-following data generated by our novel data engine, Robust Instruction Generation (RIG) engine. RIG generates two key instruction data: 1) the Adversarial Instruction-following data, which features mixed negative and positive samples to enhance the model's discriminative understanding. 2) the Diverse Instruction-following data, which contains various instruction styles to enhance model's generalization. As a result, we construct 1 million instruction-following data, consisting of 344K Adversarial samples, 508K Diverse samples, and 165K benchmark training set samples. To better handle these complex instructions, Robin3D first incorporates Relation-Augmented Projector to enhance spatial understanding, and then strengthens the object referring and grounding ability through ID-Feature Bonding. Robin3D consistently outperforms previous methods across five widely-used 3D multimodal learning benchmarks, without the need for task-specific fine-tuning. Notably, we achieve a 7.8\% improvement in the grounding task (Multi3DRefer) and a 6.9\% improvement in the captioning task (Scan2Cap).

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