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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Matterport3D is a pretty awesome dataset for RGB-D machine learning tasks :)
| Date | Stars |
|---|---|
| 2026-07-31 | 1233 |
| 2026-08-02 | 1232 |
| 2026-08-06 | 1238 |
Today
+6 stars today
This week
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Momentum
0.0
growth rate 0.00%/day
# Matterport3D
The Matterport3D V1.0 dataset contains data captured throughout 90 properties with a Matterport Pro Camera.
This repository includes the raw data for the dataset plus derived data, annotated data, and scripts/models for several scene understanding tasks.
Visit the main [website](https://niessner.github.io/Matterport) for updates and to browse the data.
## Paper
[**Matterport3D: Learning from RGB-D Data in Indoor Environments**](https://arxiv.org/abs/1709.06158)
If you use the Matterport3D data or code please cite:
```
@article{Matterport3D,
title={{Matterport3D}: Learning from {RGB-D} Data in Indoor Environments},
author={Chang, Angel and Dai, Angela and Funkhouser, Thomas and Halber, Maciej and Niessner, Matthias and Savva, Manolis and Song, Shuran and Zeng, Andy and Zhang, Yinda},
journal={International Conference on 3D Vision (3DV)},
year={2017}
}
```
## Data
The dataset consists of several types of annotations: color and depth images, camera poses, textured 3D meshes, building floor plans and region annotations, object instance semantic annotations. For details see the [data organization](data_organization.md) document.
To download the dataset, you must indicate that you agree to the terms of use by signing the [Terms of Use](http://kaldir.vc.cit.tum.de/matterport/MP_TOS.pdf) agreement form, using your institutional email addresses, and sending it to: [[email protected]](mailto:[email protected]). We will then provide download access to the dataset.
## Benchmark tasks
Using the Matterport3D data, we present several benchmark tasks: image keypoint matching, view overlap prediction, surface normal estimation, region type classification, and semantic voxel labeling. See the [tasks](tasks) directory for details.
## Tools
We provide code for loading and viewing the data. See the [code](code) directory for details.
## License
The data is released under the [Matterport3D Terms of Use](http://kaldir.vc.in.tum.de/matterport/MP_TOS.pdf), and the code is released under the MIT license.
Excerpt of 2,118 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:c4ab2c10562f634e, topic:3d-reconstruction
