In this study, we tackle the challenge of classifying the object category in
point clouds, which previous works like PointCLIP struggle to address due to
the inherent limitations of the CLIP architecture. Our approach leverages GPT-4
Vision (GPT-4V) to overcome these challenges by employing its advanced
generative abilities, enabling a more adaptive and robust classification
process. We adapt the application of GPT-4V to process complex 3D data,
enabling it to achieve zero-shot recognition capabilities without altering the
underlying model architecture. Our methodology also includes a systematic
strategy for point cloud image visualization, mitigating domain gap and
enhancing GPT-4V's efficiency. Experimental validation demonstrates our
approach's superiority in diverse scenarios, setting a new benchmark in
zero-shot point cloud classification.

我们研究了在点云中分类物体类别的挑战，通过使用 GPT-4 Vision（GPT-4V）的先进生成能力，我们的方法能够处理复杂的 3D 数据，在不改变底层模型架构的情况下实现了零样本识别能力，并通过系统策略进行点云图像可视化，提高了 GPT-4V 的效率。实验证明了我们方法在各种情景下的优越性，设定了零样本点云分类的新基准。