We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data by ensembling multiple 3D datasets and propose several strategies to automatically filter and enrich noisy text descriptions. We also explore and compare strategies for scaling 3D backbone networks and introduce a novel hard negative mining module for more efficient training. We evaluate OpenShape on zero-shot 3D classification benchmarks and demonstrate its superior capabilities for open-world recognition. Specifically, OpenShape achieves a zero-shot accuracy of 46.8% on the 1,156-category Objaverse-LVIS benchmark, compared to less than 10% for existing methods. OpenShape also achieves an accuracy of 85.3% on ModelNet40, outperforming previous zero-shot baseline methods by 20% and performing on par with some fully-supervised methods. Furthermore, we show that our learned embeddings encode a wide range of visual and semantic concepts (e.g., subcategories, color, shape, style) and facilitate fine-grained text-3D and image-3D interactions. Due to their alignment with CLIP embeddings, our learned shape representations can also be integrated with off-the-shelf CLIP-based models for various applications, such as point cloud captioning and point cloud-conditioned image generation.

翻译：我们提出了OpenShape，一种学习文本、图像和点云多模态联合表示的方法。我们采用常用的多模态对比学习框架进行表示对齐，但特别关注扩展三维表示，以实现开放世界的三维形状理解。为此，我们通过集成多个三维数据集来扩展训练数据，并提出多种策略以自动过滤和丰富嘈杂的文本描述。同时，我们探索并比较了扩展三维骨干网络的策略，并引入了一种新颖的难负例挖掘模块以提高训练效率。我们在零样本三维分类基准上评估了OpenShape，展示了其在开放世界识别中的卓越能力。具体而言，在包含1156个类别的Objaverse-LVIS基准上，OpenShape实现了46.8%的零样本准确率，而现有方法低于10%；在ModelNet40上，OpenShape达到85.3%的准确率，超越先前零样本基线方法20%，并与部分全监督方法性能相当。此外，我们证明所学嵌入编码了广泛的视觉和语义概念（如子类别、颜色、形状、风格），并促进了细粒度的文本-三维和图像-三维交互。由于这些嵌入与CLIP嵌入对齐，我们的三维形状表示可集成至现有基于CLIP的模型，应用于点云字幕生成和点云条件图像生成等任务。