Omkar2118/datasets huggingface dataset:
PARIS Dataset
Full name: PARIS Two-Part Object Dataset

From [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://arxiv.org/pdf/2308.07391):
5.1. Dataset
Synthetic dataset. The synthetic 3D models we use for evaluation are from the PartNet-Mobility dataset [49, 27, 4], a large-scale dataset for articulated objects across 46 categories. We select instances across 10 categories to conduct our experiments. For each articulation state, we randomly sample 64-100 views covering the upper hemisphere of the object to simulate capturing in the real world. Then we render RGB images and acquire camera parameters and object masks using Blender [6] to create our training data.
Real-world dataset. The real data we use for experiments is from the MultiScan dataset [25], scanning real-world indoor scenes with articulated objects in multiple states. We use the reconstructed mesh of an object in two states as ground truth for evaluation, and the real RGB frames as training data.

From the [project GitHub](https://github.com/3dlg-hcvc/paris):
We release both synthetic and real data shown in the paper [here](https://aspis.cmpt.sfu.ca/projects/paris/dataset.zip). Once downloaded, folders data and load should be put directly under the project directory. If you find it slow to download the data from our server, please try this [OneDrive](https://1sfu-my.sharepoint.com/:u:/g/personal/jla861_sfu_ca/EeEggZVIENFJm6ZEORQ8QwIBhhlY9El1amq8A9zLl0WQJA?e=Tfs0N7) link instead.

Source: [PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects](https://arxiv.org/abs/2308.07391)

Introducing paper: PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects