Photo-Geometric Autoencoder
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Oxford
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 25 November 2019
- Authors
- Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- 3D modeling, Vision
- Task
- 3D reconstruction
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 819,200,000 tokens
- Epochs
- 30
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT license: https://github.com/elliottwu/unsup3d
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 338
"Our model outperforms a current state-of-the-art 3D reconstruction method that uses 2D keypoint supervision" They don't claim absolute SOTA, only SOTA among unsupervised methods
Sources
Where this record came from and when it was last checked.
- Reference
- Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild
- Last updated
- 25 May 2026
What the numbers mean
About this model
Photo-Geometric Autoencoder was published by University of Oxford, in United Kingdom of Great Britain and Northern Ireland, in November 2019. academia is the category the publisher falls under.
It works in 3D modeling, Vision, and is recorded as doing 3D reconstruction.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
What went into building it
Around 819,200,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Photo-Geometric Autoencoder — common questions
How many parameters does Photo-Geometric Autoencoder have?
No parameter count has been published for Photo-Geometric Autoencoder, which is why no memory or speed figure appears on this page.
Who created Photo-Geometric Autoencoder?
Photo-Geometric Autoencoder was published by University of Oxford, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia.
When was Photo-Geometric Autoencoder released?
Photo-Geometric Autoencoder was published in November 2019. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is Photo-Geometric Autoencoder used for?
Photo-Geometric Autoencoder works in 3D modeling, Vision, and is recorded as handling 3D reconstruction. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download Photo-Geometric Autoencoder?
The weights for Photo-Geometric Autoencoder are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run Photo-Geometric Autoencoder?
We cannot say. Photo-Geometric Autoencoder has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is Photo-Geometric Autoencoder open source?
Its weights are published, so Photo-Geometric Autoencoder can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.