  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.
