PointCoT: A Multi-modal Benchmark for Explicit 3D Geometric Reasoning
This addresses the problem of geometric hallucinations in 3D AI models for researchers and practitioners in computer vision and robotics, representing a novel method rather than an incremental improvement.
The paper tackles the challenge of extending multimodal large language models to 3D point cloud understanding by introducing PointCoT, a framework that uses explicit Chain-of-Thought reasoning to reduce geometric hallucinations, achieving state-of-the-art performance on complex reasoning tasks.
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.