Point-e
Point·E is OpenAI's archived open-source 3D point cloud generation model for text-to-3D and image-to-3D synthesis.
Clone Point·E if you want to understand diffusion applied to 3D data or need a reference for P-FID and P-IS evaluation. Skip it if you need a 3D asset today—the repo was archived October 22, 2023, so there is no maintenance, no official support, and no future updates. For actual asset production, use a maintained commercial generator like Meshy or Rodin.
Verified 1d ago · liveness 66/100 · cite: rightaichoice.com/tools/point-e
- Researchers studying diffusion models applied to 3D data
- Developers forking a modular text-to-3D or image-to-3D pipeline
- Students learning how point cloud synthesis works end to end
- Teams benchmarking their own 3D generators against P-FID and P-IS baselines
- Anyone needing production-quality 3D assets or clean meshes
- Users who want a GUI, web app, or hosted service
- Real-time or interactive 3D generation
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Skip Point·E if you need a maintained tool or a finished 3D asset — it was archived on October 22, 2023 and its text-to-3D model only handles simple categories and colors.
There is no license fee, but you pay in compute — sampling runs on a CUDA GPU you supply yourself, and the repo provides no hosted fallback.
Point·E is free under the MIT license — $0, no tiers, no seats, no usage caps. That puts it below every commercial 3D generator: tools like Meshy charge recurring subscriptions for hosted generation, while Point·E costs nothing but the GPU you run it on and the engineering time you spend maintaining a fork that OpenAI archived in October 2023.
In short
Point-e — Point·E is OpenAI's archived open-source 3D point cloud generation model for text-to-3D and image-to-3D synthesis. Best for Researchers studying diffusion models applied to 3D data, Developers forking a modular text-to-3D or image-to-3D pipeline, Students learning how point cloud synthesis works end to end. Free to use.
What people actually say about Point-e — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
81 mentions across 5 sources (Hacker News, YouTube, Product Hunt, GitHub, Lemmy) · researched Aug 25, 2026.
Average across the 5 sources that answered — each source counts once, not each post.
- +Fast generation: one 3D sample in about a minute, far quicker than competitors
- +Open source with MIT license, free for commercial use
- +Modular pipeline: text-to-image, point cloud diffusion, and mesh conversion
- +Includes pre-trained weights and Jupyter notebooks for each stage
- +Scriptable Python API and CLI for batch processing and prototyping
- −Output is sparse point cloud, not a refined mesh asset
- −Mesh conversion lacks color and texture (reported on GitHub)
- −Text-to-3D only understands simple categories and colors
- −Installation unclear: 'pip install -e' confuses users
- −Dependency issues with skimage and marching_cubes break notebooks
- • No direct hidden costs, but you need a GPU with at least 8GB VRAM for comfortable inference
Viability Score
How well maintained and how widely used is Point-e? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Text-to-3D point cloud generation (limited to simple categories and colors)
- Image-to-3D point cloud generation conditioned on synthetic view images
- Point cloud to mesh conversion via SDF regression model
- Diffusion model that synthesizes point clouds directly
- Ships with pre-trained model weights
- Jupyter notebooks: image2pointcloud.ipynb, text2pointcloud.ipynb, pointcloud2mesh.ipynb
- evaluate_pfid.py and evaluate_pis.py compute P-FID and P-IS metrics
- blender_script.py renders point clouds and meshes
- MIT license permits commercial use and modification
- Install via pip install -e .
- Python API for sampling and inference
- Command-line sampling interface
- Conditions generation on CLIP embeddings
- CUDA GPU acceleration for sampling
About Point-e
Point·E is the official code and model release for OpenAI's paper "Point-E: A System for Generating 3D Point Clouds from Complex Prompts." It is not a hosted product: you clone the GitHub repo, run pip install -e ., and work through Jupyter notebooks locally. Three notebooks cover the core workflow: image2pointcloud samples a point cloud conditioned on synthetic view images, text2pointcloud runs the pure text-to-3D model, and pointcloud2mesh applies an SDF regression model to convert point clouds into meshes. Separate scripts, evaluate_pfid.py and evaluate_pis.py, compute P-FID and P-IS metrics, while blender_script.py renders point clouds and meshes. The diffusion model synthesizes point clouds directly, conditioning generation on CLIP embeddings, and ships with pre-trained weights. It is MIT-licensed and free to use, modify, and fork, with a Python API and command-line sampling interface plus CUDA GPU acceleration. Quality expectations matter: OpenAI describes the pure text-to-3D model as "small, worse quality," understanding only some simple categories and colors, and output is a sparse point cloud rather than a finished asset. The repository was archived on October 22, 2023 and is no longer maintained, so there are no future updates, no official support, and a frozen dependency set. For production 3D assets, look at maintained commercial generators like Meshy or Rodin instead. Point·E's value now is as a reference implementation: study it, fork it, benchmark against its P-FID and P-IS scripts.
Behind the Verdict
When to pick this: you are researching diffusion models applied to 3D data, teaching point cloud synthesis end to end, or forking a modular text-to-3D or image-to-3D pipeline. The three notebooks (image2pointcloud, text2pointcloud, pointcloud2mesh) and the evaluation scripts give you a complete reference implementation you can run locally. When to pass: you need production-quality 3D assets, clean meshes, a GUI or web app, or real-time/interactive generation. The text-to-3D model is explicitly "small, worse quality" and limited to simple categories and colors, and output is a sparse point cloud rather than a finished asset. The repo was archived on October 22, 2023, so there is no maintenance, no official support, and no future updates. Compared to alternatives: maintained commercial generators like Meshy or Rodin are the realistic choice for asset production. Point·E remains a frozen reference implementation worth studying, forking, and benchmarking against its P-FID and P-IS scripts. Real-world caveats: expect dependency drift and environment friction because the repo is frozen; the MIT license permits commercial use and modification, but the archived state means you own any fixes. We would reach for this when the goal is understanding or evaluation, not shipping assets.
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Real-world workflow fit
Concrete scenarios for the personas Point-e actually fits — and what changes day-one when you adopt it.
You clone the repo, run pip install -e ., and use evaluate_pfid.py and evaluate_pis.py to compute P-FID and P-IS on your own model's outputs against Point·E as a baseline.
Outcome: You get a reproducible, citable baseline with published metric scripts, without paying for a commercial evaluation service.
You open text2pointcloud.ipynb, prompt for a simple object like a chair, and step through the sampling code to see how CLIP embeddings condition the diffusion model.
Outcome: You end up with a working point cloud and a concrete understanding of the pipeline you can fork for your own experiments.
You generate a point cloud with image2pointcloud.ipynb, convert it with pointcloud2mesh.ipynb, and render the result with blender_script.py.
Outcome: You have a rendered mesh and point cloud for a paper or class project, using only free, MIT-licensed code.
Use Cases
- Generate a 3D point cloud of a chair from a text description.
- Create a 3D point cloud conditioned on a set of rendered images of an object.
- Convert a raw point cloud into a watertight mesh using the provided SDF model.
- Evaluate generative 3D models using P-FID and P-IS metrics.
- Render 3D point clouds with Blender for visual inspection.
Limitations
- The pure text-to-3D model only understands simple categories and colors, and OpenAI's own README calls it "small, worse quality." Output is a sparse point cloud, not a refined asset.
- The repository was archived on October 22, 2023 and is no longer maintained — no bug fixes, no dependency updates, no official support, and 64 open issues and 16 open pull requests that will not be triaged.
- There is no GUI, web app, or hosted service; you run the notebooks and scripts yourself, which requires a CUDA GPU for practical sampling times.
as of 2026-10-02
Verification history
We have re-verified Point-e 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Point-e tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Where the pricing makes sense
The company stage and team size where Point-e's pricing actually pencils out — and where peers do it cheaper.
Point·E is free under the MIT license — $0, no tiers, no seats, no usage caps. That puts it below every commercial 3D generator: tools like Meshy charge recurring subscriptions for hosted generation, while Point·E costs nothing but the GPU you run it on and the engineering time you spend maintaining a fork that OpenAI archived in October 2023.
Setup time & first value
How long it actually takes to get something useful out of Point-e — broken out by persona, not the marketing-page minute.
Researchers and developers comfortable with Python and CUDA should reach a first sample in roughly 30–60 minutes: clone the repo, run pip install -e ., download the pre-trained weights, and open a notebook. Students new to the stack should budget half a day to work through the install and the first notebook. There is no signup, no onboarding flow, and no vendor support if the install breaks.
Switching to or from Point-e
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From custom diffusion research code: swap your sampler for the Point·E notebooks and reuse evaluate_pfid.py and evaluate_pis.py as the metric layer.
- →From a commercial 3D generator: only migrate if you want to study or fork the method — Point·E output is a sparse point cloud, not a production asset.
- ↗To Meshy: move to a hosted generator when you need production-quality meshes and no longer want to maintain a fork of an archived repo.
- ↗To Rodin: switch when you need refined 3D assets rather than sparse point clouds generated by a small text-to-3D model.
- ↗To your own maintained fork: keep the MIT-licensed code and weights but take over dependency updates, since OpenAI archived the repository on October 22, 2023.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Point-e”, and we withheld 6: 6 could not be judged, because “Point-e” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Point-e.
Official links
Tools that pair well with Point-e
Common stack mates teams adopt alongside Point-e, with the specific reason each pairing earns its keep.
Infinigen
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GET3D by NVIDIA
Open-source NVIDIA research model that generates textured 3D meshes from 2D image collections with no 3D supervision.
Sloyd
Sloyd turns text prompts and images into game-ready 3D models with unlimited generations on a flat subscription.
Featured Head-to-Head Comparisons
Point E vs Adobe Firefly Services
Point-E is a free, open-source research tool for 3D point cloud generation, best for experimentation. Adobe Firefly Services is a paid, enterprise-grade API suite for 2D image generation and editing with compliance and scalability. Choose Point-E if you need 3D outputs and are cost-constrained; choose Firefly Services for reliable, safe image generation at scale.
Point E vs Qoves
Choose Point-E if you're a researcher or developer exploring 3D generative AI for free. Choose QOVES if you want a personalized, science-backed facial analysis for non-surgical improvement. They solve completely different problems.
Point E vs The New Black
Choose Point·E if you're a researcher or developer diving into 3D generative modeling and need a free, open-source starting point. Choose The New Black if you're a fashion professional seeking a polished, industry-specific tool to generate production-ready apparel designs quickly. For most commercial fashion needs, The New Black is the clear winner; for 3D research and prototyping, Point·E offers unique value at no cost.
Alternatives to Point-e
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Infinigen
Open-source procedural generation that renders photorealistic 3D worlds and labeled synthetic training data from Python code alone.
GET3D by NVIDIA
Open-source NVIDIA research model that generates textured 3D meshes from 2D image collections with no 3D supervision.
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