Robin3D: Improving 3D Large Language Model via Robust Instruction Tuning
Authors: name: Weitai Kang, name: Haifeng Huang, name: Yuzhang Shang, name: Mubarak Shah, name: Yan Yan

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).