给3D视觉和LLM交叉领域的研究者看，新数据集加新模型，还做了两个实际应用基准，能补上多物体关系推理这块短板。
现有3D大语言模型难以进行多物体间的细粒度比较。研究者提出Multi-3DLLM框架，包含多物体指令数据集MO3D、最小化Patch交互Transformer（PIT）以及Shape Mating和Change Captioning两个应用基准。实验显示Multi-3DLLM在MO3D上超越所有基线，并正向迁移到单物体分类。
Beyond Single Object: Learning 3D Relations with Large Language Models
We address a fundamental gap in 3D-LLMs: existing models focus on single-object/scene description, struggling with detailed, inter-object comparison. We propose a framework for detailed object-level reasoning across multiple objects with three components: (1) MO3D (Multi-Object in 3D), an instruction dataset requiring fine-grained multi-object comparison; (2) Multi-3DLLM, using a minimal Patch-Interaction Transformer (PIT) that models inter-/intra-object relationships while preserving local geometry; (3) Mini-apps, two application-driven benchmarks (Shape Mating, Change Captioning) that probe geometric understanding for practical use. Recent 3D-LLMs and 2D-VLMs perform poorly on these tasks, lacking both comparison-centric design and geometric awareness. In contrast, Multi-3DLLM trained on our mixture data learns geometric reasoning, surpasses all baselines on MO3D, and provides positive transfer to single-object classification.