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language: |
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- en |
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task_categories: |
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- summarization |
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tags: |
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- 3d meshes |
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- point clouds |
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- synthetic |
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- realistic |
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- CAD |
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- statues |
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pretty_name: Points2Surf Dataset |
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size_categories: |
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- 1K<n<10K |
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--- |
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We introduced this dataset in Points2Surf, a method that turns point clouds into meshes. |
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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). |
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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. |
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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. |
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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. |
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The training set consists of 4950 _ABC_ objects with varying number of scans and noise strength. |
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The validation sets are the same as the test set. |
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The _ABC_ test sets contain 100 objects, _Famous_ 22 and _Thingi10k_ 100. The test set variants are as follows: |
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(1) _ABC_ var (like training set), no noise, strong noise; |
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(2) _Famous_ no noise, medium noise, strong noise, sparse, dense scans; |
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(3) _Thingi10k_ no noise, medium noise, strong noise, sparse, dense scans |
