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arXiv · 2510.16714

SceneCOT: Eliciting Grounded Chain-of-Thought Reasoning in 3D Scenes

Xiongkun Linghu·Jiangyong Huang·Ziyu Zhu·Baoxiong Jia·Siyuan Huang
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Abstract

Existing research on 3D Large Language Models (LLMs) still struggles to achieve grounded question-answering, primarily due to the under-exploration of the mechanism of human-like scene-object grounded reasoning. This paper bridges the gap by presenting a novel framework. We first introduce a grounded Chain-of-Thought reasoning method in 3D scenes (SCENECOT), decoupling a complex reasoning task into simpler and manageable problems, and building corresponding visual clues based on multimodal expert modules. To enable such a method, we develop SCENECOT-185K, the first large-scale grounded CoT reasoning dataset, consisting of 185K high-quality instances. Extensive experiments across various complex 3D scene reasoning benchmarks demonstrate that our new framework achieves strong performance with high grounding-QA coherence. To the best of our knowledge, this is the first successful application of CoT reasoning to 3D scene understanding, enabling step-by-step human-like reasoning and showing potential for extension to broader 3D scene understanding scenarios.

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Xiongkun Linghu, Jiangyong Huang, Ziyu Zhu, Baoxiong Jia, Siyuan Huang. 2026-03-05. SceneCOT: Eliciting Grounded Chain-of-Thought Reasoning in 3D Scenes. https://arxiv.org/abs/2510.16714

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