SceneCOT: Eliciting Chain-of-Thought Reasoning in 3D Scenes
Xiongkun Linghu, Jiangyong Huang, Ziyu Zhu, Baoxiong Jia, Siyuan Huang
Abstract
Existing research of 3D LLMs still struggles to achieve efficient and explainable reasoning, 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 Chain-of-Thought reasoning framework 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 framework, we build the first large-scale 3D scene Chain-of-Thought reasoning dataset, SceneCOT, including more than 190k high-quality data instances. Extensive experiments across various complex 3D scene reasoning benchmarks demonstrate that our new framework achieves state-of-the-art with clear interpretability. To our knowledge, this is the first attempt to successfully implement the COT technique for achieving human-like step-by-step reasoning for 3D scene understanding, where we show great potential in extending it to a wider range of 3D scene understanding scenarios.
BibTeX
@inproceedings{
linghu2026scenecot,
title={Scene{COT}: Eliciting Chain-of-Thought Reasoning in 3D Scenes},
author={Xiongkun Linghu and Jiangyong Huang and Ziyu Zhu and Baoxiong Jia and Siyuan Huang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=U9meoc0Sau}
}