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license: mit
task_categories:
- other
language:
- en
tags:
- topology
- geometric deep-learning
- point clouds
pretty_name: Dataset Of ManNifold strUcTures
size_categories:
- 10K<n<100K
---
# DONUT (Dataset Of MaNifold strUcTures)
This repository contains a dataset of 3D samples made of watertight meshes and corresponding point clouds. Each sample is composed of one or several watertight mesh components and one 8192-point cloud representation.
The dataset contains **29,517 samples** in total.
## Overview
The figure below shows a few samples from the dataset together with their labels.
## Contents
The repository is organized as follows:
```text
.
├── metadata.csv
├── obj/
│ ├── shard_0/
│ ├── shard_1/
│ └── shard_2/
└── pcd/
├── shard_0/
├── shard_1/
└── shard_2/
```
- `obj/` contains the mesh files as `.npz` archives.
- `pcd/` contains the point clouds as `.npy` files.
- `metadata.csv` contains one row per sample with topological metadata.
There are **29,517** mesh files in `obj/` and **29,517** point cloud files in `pcd/`.
## File Format
### Meshes
Each mesh sample is stored as an `.npz` file in `obj/`. The archive contains:
- `vertices.npy`
- `faces.npy`
A sample may contain one or several watertight connected mesh components.
### Point Clouds
Each point cloud sample is stored as a `.npy` file in `pcd/`.
Each point cloud contains **8192 points** and corresponds to the sample with the same `id`.
## Sample Identification
Each sample is identified by a unique `id` string.
The same `id` is used in:
- the filename in `obj/`
- the filename in `pcd/`
- the `id` column in `metadata.csv`
For example, if a sample has id `abc123`, its files are:
- `obj/.../abc123.npz`
- `pcd/.../abc123.npy`
## Metadata
`metadata.csv` contains the following columns:
- `id`: unique identifier of the sample
- `genus`: total number of holes across all mesh components in the sample
- `components`: total number of connected mesh components in the sample
- `sample_code`: array of 6 integers describing how many components of each genus are present
## Meaning of `sample_code`
`sample_code` is an array of 6 integers:
```text
[n0, n1, n2, n3, n4, n5]
```
Here, `ni` is the number of mesh components in the sample whose genus is `i`.
So:
- `n0` is the number of genus-0 components
- `n1` is the number of genus-1 components
- `n2` is the number of genus-2 components
- `n3` is the number of genus-3 components
- `n4` is the number of genus-4 components
- `n5` is the number of genus-5 components
From `sample_code`, the metadata values are computed as:
```text
genus = sum(i * ni for i in [0, 1, 2, 3, 4, 5])
components = sum(ni for i in [0, 1, 2, 3, 4, 5])
```
In other words:
- `genus` is the total number of holes in the full sample
- `components` is the total number of connected components in the full sample
The distribution of labels in the dataset is shown below.
## Examples
```text
sample_code = [2, 1, 0, 0, 0, 0]
```
This means:
- 2 components of genus 0
- 1 component of genus 1
- total genus = 0 * 2 + 1 * 1 = 1
- total components = 2 + 1 = 3
## Summary
DONUT is a dataset of **29,517** samples of manifold 3D structures.
Each sample provides:
- one mesh file in `.npz` format
- one 8192-point cloud in `.npy` format
- one metadata entry in `metadata.csv`
The metadata describes the global topology of each sample through its total genus, number of connected components, and component-wise genus distribution.
## Citation
If you use DONUT, please cite the paper [FILTR: Extracting Topological Features from Pretrained 3D Models](https://arxiv.org/abs/2604.22334):
```bibtex
@inproceedings{Martinez2026FILTR,
title={FILTR: Extracting Topological Features from Pretrained 3D Models},
author={Louis Martinez and Maks Ovsjanikov},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2026},
url={https://arxiv.org/abs/2604.22334}
}
```
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