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

TL;DR

This paper introduces a novel method leveraging GPT-4 Vision's generative capabilities for zero-shot classification of point clouds, overcoming limitations of previous models like PointCLIP and setting new benchmarks.

Contribution

It adapts GPT-4V for 3D point cloud data, enabling zero-shot recognition without model architecture changes and improving robustness through visualization strategies.

Findings

Outperforms existing methods in diverse scenarios

Achieves state-of-the-art zero-shot classification accuracy

Demonstrates robustness and adaptability in complex 3D data

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.