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

TL;DR

PointCoT introduces an explicit reasoning framework for 3D point cloud understanding in multimodal models, significantly improving geometric reasoning accuracy by generating structured rationales before final answers.

Contribution

The paper presents PointCoT, a novel multi-modal framework with explicit Chain-of-Thought reasoning and a large-scale benchmark for 3D geometric understanding.

Findings

Achieves state-of-the-art results on complex 3D reasoning tasks.

Demonstrates the effectiveness of explicit reasoning over implicit methods.

Constructed a large-scale benchmark with hierarchical CoT annotations.

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.