Objaverse Web 3D Objects

Web-sourced 3D objects with captions at large scale, the messy open ground for text-to-3D and open shape learning.

Objaverse pairs each web 3D object with captions, so text prompts learn to retrieve and generate shapes

Why Does This Exist?

Curated CAD sets are clean but small and narrow. Open text-to-3D needs breadth with language attached. Objaverse gathered web 3D objects with captions at large scale, trading curation for coverage so open shape models could train where ShapeNet stops.

This page covers captioned-3D roles and appropriate use. Verify the release before quoting object counts.

Think of It Like This

A flea market with price tags

Curated CAD is a boutique with perfect lighting. Objaverse is a flea market: vast, uneven, everything tagged with a handwritten note.

You find rare treasures there, but you check quality before buying.

How It Actually Works

Purpose is open 3D retrieval and generation with text. Inputs are objects plus captions, outputs are retrieved shapes or generated views by task. Appropriate use is open-vocabulary 3D research and pretraining. Quality variance and license mix demand filtering and rights checks before product use.

Toy retrieval walkthrough

Take 10 captioned objects where text search returns 5 and 4 match. Precision is $4 / 5 = 0.8$ and recall against 6 relevant is $4 / 6 \approx 0.667$ . Breadth helps recall while noise taxes precision.

Code

Precision and recall on toy retrieval hits.

1def pr(hits: int, returned: int, relevant: int) -> tuple[float, float]:2    return round(hits / max(returned, 1), 3), round(hits / max(relevant, 1), 3)3
4
5print(pr(4, 5, 6))6# -> (0.8, 0.667)

Four of five returned with six relevant gives 0.8 and about 0.667.

Watch Out For

Ignoring license and quality mix

Web objects mix licenses and broken geometry. The symptom is a demo that cannot ship. Fix it by filtering licenses and manifold checks before training products.

The Quick Version

Objaverse maps web 3D objects to captions for open shape work.
Inputs are objects plus text, outputs are retrieved or generated shapes.
Toy 4 of 5 with 6 relevant gives 0.8 precision and about 0.667 recall.
Use it for open 3D research beyond curated CAD.
Filter quality and licenses before product use.