  In recent years, 3D representation learning has turned to 2D vision-language
pre-trained models to overcome data scarcity challenges. However, existing
methods simply transfer 2D alignment strategies, aligning 3D representations
with single-view 2D images and coarse-grained parent category text. These
approaches introduce information degradation and insufficient synergy issues,
leading to performance loss. Information degradation arises from overlooking
the fact that a 3D representation should be equivalent to a series of
multi-view images and more fine-grained subcategory text. Insufficient synergy
neglects the idea that a robust 3D representation should align with the joint
vision-language space, rather than independently aligning with each modality.
In this paper, we propose a multi-view joint modality modeling approach, termed
JM3D, to obtain a unified representation for point cloud, text, and image.
Specifically, a novel Structured Multimodal Organizer (SMO) is proposed to
address the information degradation issue, which introduces contiguous
multi-view images and hierarchical text to enrich the representation of vision
and language modalities. A Joint Multi-modal Alignment (JMA) is designed to
tackle the insufficient synergy problem, which models the joint modality by
incorporating language knowledge into the visual modality. Extensive
experiments on ModelNet40 and ScanObjectNN demonstrate the effectiveness of our
proposed method, JM3D, which achieves state-of-the-art performance in zero-shot
3D classification. JM3D outperforms ULIP by approximately 4.3% on PointMLP and
achieves an improvement of up to 6.5% accuracy on PointNet++ in top-1 accuracy
for zero-shot 3D classification on ModelNet40. The source code and trained
models for all our experiments are publicly available at
https://github.com/Mr-Neko/JM3D.

Computer Vision and Pattern Recognition