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

TL;DR

This work tackles zero-shot point cloud classification, a setting where CLIP-based approaches struggle due to domain gaps between 3D visualizations and text. It proposes to leverage GPT-4V by rendering 3D point clouds into RGB images from three views and prompting the model with a predefined category template, effectively solving $f: x \in \mathbb{R}^{K\times3} \rightarrow l \in \{1,\dots,C\}$ without modifying the underlying model. The study systematically analyzes visualization variants (e.g., $I \in \mathbb{R}^{H\times W\times3}$ from DM-sparse, DM-dense, RI-colored, RI-gray renderings) and determines that grayscale RI rendering with multiple views yields the best performance, achieving state-of-the-art zero-shot accuracy on ModelNet10/ModelNet40, with a notable 6.7 percentage-point gain on ModelNet10. A key takeaway is the potential to adapt powerful vision-language models for 3D understanding without architectural changes, balanced against slower inference times and dependence on prompt design and rendering quality.

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.

Figures (5)

• Figure 1: Illustration for our method. Using three-view point cloud rendered images and a predefined text template as input, GPT-4V will analyse the visual clue like human then point out the category.
• Figure 2: 3D point cloud (left) and four different point cloud visualization methods (right).
• Figure 3: Qualitative results: comparison with the-state-of-the-art methods zhu2022pointclipzhang2022pointclip. GPT-4V makes the right choice while the previous methods fail to do so. Note that the colored image is for point cloud visualization, not for model input.
• Figure 4: Qualitative results: comparison with the influence of different visualization methods on the results of GPT-4V decision. Among four visualizations, only rendered gray image helps GPT-4V make the right classification.
• Figure 5: Frequent failure cases for GPT-4V. Case 1: GPT-4V identifies the backreset as the mean feature of chair, neglecting the possibility to be toilet tank. Case 2: GPT-4V can hardly distinguish dresser, night stand or table with the nearly rectangular cuboid point cloud alone.