PointNet
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 2 December 2016
- Authors
- CR Qi, H Su, K Mo, LJ Guibas
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- 3D modeling
- Task
- 3D segmentation
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
- 9,843 tokens
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
- Highly cited
- Record confidence
- Unknown
- Citations
- 17,492
Sources
Where this record came from and when it was last checked.
- Reference
- PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
PointNet was published by Stanford University, in United States of America, in December 2016. The organisation is categorised as academia.
It works in 3D modeling, and is recorded as doing 3D segmentation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
The training set ran to roughly 9,843 tokens.
The reason it appears in this catalogue at all is highly cited.
Answers
PointNet — common questions
Is PointNet open source?
The licensing for PointNet was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does PointNet have?
No parameter count has been published for PointNet, which is why no memory or speed figure appears on this page.
Who created PointNet?
PointNet was published by Stanford University, based in United States of America, categorised as academia.
When was PointNet released?
PointNet was published in December 2016. 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 PointNet used for?
PointNet works in 3D modeling, and is recorded as handling 3D segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run PointNet?
None. PointNet is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
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.