Point·E

✓ verified, sync 2026-08-20

11 checks on record, most recent 2026-11-02 (responded).

Community scorenot yet rated

First indexed2026-08-20

Observation record.

What was checked ourselves, with dates. Not vendor copy, not a rating.

Address confidence: low. The URL on file may not be Point·E's own domain (for example a marketplace or parked host). The dates below describe checks against that URL; the link between that URL and Point·E itself is not confirmed.

Most recent check 2026-11-02

Result: responded.

Checked on 11 distinct dates, since 2026-08-30. Checked roughly every 6.4 nights on this listing's own record.

2026-11-022026-10-272026-10-212026-10-152026-10-092026-10-032026-09-272026-09-212026-09-152026-09-032026-08-30

What this means: the 3-miss rule marks a listing discontinued only after three consecutive failed checks; a bot-wall response never counts toward that streak. the methodology

Price events.

auto-diffed on every sync: the changelog other directories don't keep

The price was checked on 2026-08-30 and no change was observed since.

No approved price events on record

No approved price change is on record.

What it does.

The vendor's own description, unedited, not our claim.

OpenAI's Point-E is an AI tool for synthesizing 3D models from point clouds. It uses a diffusion algorithm to transform point clouds into 3D models and is designed to create detailed, realistic models. Point-E is available as an open source project on GitHub and is released under the MIT license. It uses a variety of tools and packages, such as GitHub Actions and Codespaces, to automate workflows and create instant development environments. It also features a variety of features, such as code review and issues tracking, to help ensure high quality and efficient code. Point-E also includes a model-card for describing the model used for synthesis and a setup.py for installing the package. To use Point-E, users can clone the repository via HTTPS, GitHub CLI or SVN, and launch GitHub Desktop, Xcode or Visual Studio Code to get started. It can then be used to generate 3D models from complex point clouds, with the output being highly realistic and detailed.

registry ID ndock:tool:pointe