Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding

• URL: http://arxiv.org/abs/2401.07572v1
• Date: Mon, 15 Jan 2024 10:16:44 GMT
• Title: Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding
• Authors: Qi Sun, Xiao Cui, Wengang Zhou and Houqiang Li
• Abstract summary: We tackle the challenge of classifying the object category in point clouds. We employ GPT-4 Vision (GPT-4V) to overcome these challenges. We set a new benchmark in zero-shot point cloud classification.
• Score: 114.4754255143887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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