PointCoT: A Multi-modal Benchmark for Explicit 3D Geometric Reasoning

2026-02-27

AI Summary

This work addresses the limited explicit geometric reasoning capability of existing 3D multimodal large language models, which often generate geometric hallucinations inconsistent with structural details. To overcome this, we propose an “Observe–Think–Answer” paradigm that introduces, for the first time, an explicit chain-of-thought mechanism into 3D understanding. Our approach supervises the generation of geometry-based intermediate reasoning chains before producing final answers, enabling structured reasoning over point cloud data. To support this, we construct Point-Reason-Instruct, a large-scale instruction-tuning benchmark comprising 86,000 hierarchically annotated reasoning samples, and design a dual-stream multimodal architecture to effectively fuse semantic and geometric information. Experiments demonstrate that our method achieves state-of-the-art performance on complex 3D geometric reasoning tasks, significantly mitigating geometric hallucinations and enhancing fidelity to structural details.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: 3D Computer Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

While Multimodal Large Language Models (MLLMs) demonstrate proficiency in 2D scenes, extending their perceptual intelligence to 3D point cloud understanding remains a significant challenge. Current approaches focus primarily on aligning 3D features with pre-trained models. However, they typically treat geometric reasoning as an implicit mapping process. These methods bypass intermediate logical steps and consequently suffer from geometric hallucinations. They confidently generate plausible responses that fail to ground in precise structural details. To bridge this gap, we present PointCoT, a novel framework that empowers MLLMs with explicit Chain-of-Thought (CoT) reasoning for 3D data. We advocate for a \textit{Look, Think, then Answer} paradigm. In this approach, the model is supervised to generate geometry-grounded rationales before predicting final answers. To facilitate this, we construct Point-Reason-Instruct, a large-scale benchmark comprising $\sim$86k instruction-tuning samples with hierarchical CoT annotations. By leveraging a dual-stream multi-modal architecture, our method synergizes semantic appearance with geometric truth. Extensive experiments demonstrate that PointCoT achieves state-of-the-art performance on complex reasoning tasks.

Problem

• 3D geometric reasoning
• multimodal large language models
• geometric hallucination
• point cloud understanding
• Chain-of-Thought

Innovation

• Chain-of-Thought Reasoning
• 3D Point Cloud Understanding
• Multimodal Large Language Models
• Geometric Reasoning
• Explicit Reasoning