---
language:
- en
task_categories:
- summarization
tags:
- 3d meshes
- point clouds
- synthetic
- realistic
- CAD
- statues
pretty_name: Points2Surf Dataset
size_categories:
- 1K<n<10K
---

We introduced this dataset in Points2Surf, a method that turns point clouds into meshes.

It consists of objects from the [_ABC Dataset_](https://paperswithcode.com/dataset/abc-dataset-1), a collection of _Famous_ meshes and objects from [_Thingi10k_](https://paperswithcode.com/dataset/thingi10k).
These are mostly single objects per file, sometimes a couple of disconnected objects. Objects from the _ABC Dataset_ are CAD-models, the others are mostly statues with organic structures.

We created realistic point clouds using a simulated time-of-flight sensor from [_BlenSor_](https://www.blensor.org/). The point clouds have typical artifacts like noise and scan shadows.

Finally, we created training data consisting of randomly sampled query points with their ground-truth signed distance. The query points are 50% uniformly distributed in the unit cube and 50% near the surface with some random offset.

The training set consists of 4950 _ABC_ objects with varying number of scans and noise strength. 
The validation sets are the same as the test set. 
The _ABC_ test sets contain 100 objects, _Famous_ 22 and _Thingi10k_ 100. The test set variants are as follows:
(1) _ABC_ var (like training set), no noise, strong noise; 
(2) _Famous_ no noise, medium noise, strong noise, sparse, dense scans;
(3) _Thingi10k_ no noise, medium noise, strong noise, sparse, dense scans