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
- 7 June 2017
- Authors
- Charles R. Qi, Li Yi, Hao Su, Leonidas J. 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
- Numerical format
- FP32
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
- 60,000 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
- 14,031
Sources
Where this record came from and when it was last checked.
- Reference
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
- Last updated
- 25 May 2026
What the numbers mean
What this model is
PointNet++ was published by Stanford University, in the country recorded as United States of America, during June 2017. The publishing organisation is categorised as academia.
It works in the domain of 3D modeling, and is recorded as performing the task of 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
Training consumed a corpus of around 60,000 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
PointNet++ — common questions
PointNet++— what is it used for?
It works in the domain of 3D modeling, and is recorded as handling the task of 3D segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
PointNet++— what GPU do I need to run it?
None. This 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.
PointNet++— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
PointNet++— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
PointNet++— who created it?
It was published by Stanford University, based in United States of America, an organisation categorised as academia.
PointNet++— when was it released?
It was published in June 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.