---
title: ShapeR-Evaluation
canonical_url: "https://www.modelscope.cn/datasets/facebook/ShapeR-Evaluation"
md_url: "https://www.modelscope.cn/datasets/facebook/ShapeR-Evaluation.md"
repository: facebook/ShapeR-Evaluation
last_updated: 2026-01-21
license: cc-by-nc-4.0
storage_size: "26 GB"
downloads: 492
stars: 1
---

# ShapeR-Evaluation

> ShapeR-Evaluation - facebook 在 ModelScope 开源的数据集。ShapeR Evaluation Dataset

facebook/ShapeR-Evaluation 是 ModelScope 魔搭社区上的数据集，存储大小 26 GB，采用 cc-by-nc-4.0 许可。

- **Repository**: facebook/ShapeR-Evaluation
- **License**: cc-by-nc-4.0
- **Storage size**: 26 GB
- **Downloads**: 492
- **Stars**: 1
- **Last updated**: 2026-01-21

Source: https://www.modelscope.cn/datasets/facebook/ShapeR-Evaluation

---

# ShapeR Evaluation Dataset

![Example visualization](resources/dataset_annotation_process.jpg)

We introduce a new dataset of in-the-wild sequences with paired posed multi-view images, SLAM
point clouds, and individually complete 3D shape annotations for 178 objects across 7 diverse scenes. In contrast to existing real-world 3D reconstruction datasets which are either captured in controlled setups or have merged object and background geometries or incomplete shapes, this dataset is designed to capture real-world challenges like occlusions, clutter, and variable resolution and viewpoints to enable realistic, in-the-wild evaluation.

[Project Page](http://facebookresearch.github.io/ShapeR)  |  [Paper](https://huggingface.co/papers/2601.11514)  |  [Code](https://github.com/facebookresearch/ShapeR) | [Video](https://www.youtube.com/watch?v=EbY30KAA55I)  |  [HF-Model](https://huggingface.co/facebook/ShapeR/)  |  [HF Evaluation Dataset](https://huggingface.co/datasets/facebook/ShapeR-Evaluation)

## Usage

Clone the [repository](https://github.com/facebookresearch/ShapeR) and follow the INSTALL.md instructions to install the required dependencies.

To run inference on a sample from the dataset:
```bash
python infer_shape.py --input_pkl <sample.pkl> --config balance --output_dir output
```

## Examples

![teaser](resources/dataset_samples.jpg)

The dataset contains 178 objects across seven casually-captured recordings from distinct cluttered scenes annotated high quality 3D geometry. It covers a wide range of categories, from large objects like furniture to smaller items such as remotes, toasters, and tools. For each sequence, we provide multi-view images, calibrated camera parameters, SLAM point clouds, and machine-generated object captions. Each annotated object also includes a complete reference mesh generated using internal image-to-3D modeling methods under ideal conditions, which we manually refined and realigned for geometric and pose consistency.

## Data Format

[**ShapeR Evaluation Dataset**](https://huggingface.co/datasets/facebook/ShapeR-Evaluation) contains preprocessed samples from Aria glasses captures, where each sample is a pickle file with point clouds, multi-view images, camera parameters, text captions, and ground truth meshes.

For a detailed walkthrough of the data format, see the **[`explore_data.ipynb`](https://github.com/facebookresearch/ShapeR/blob/main/explore_data.ipynb)** notebook which includes:
- Complete pickle file structure with all keys and their dimensions
- Interactive 3D visualization of point clouds and meshes
- Camera position visualization
- Image and mask grid displays
- DataLoader usage examples for both SLAM and RGB variants
- Explanation of view selection strategies

## License

ShapeR evaluation dataset is licensed under CC-BY-NC. See [LICENSE](LICENSE) for details.

## Citation

If you find ShapeR useful for your research, please cite our paper:

```bibtex
@misc{siddiqui2026shaperrobustconditional3d,
      title={ShapeR: Robust Conditional 3D Shape Generation from Casual Captures}, 
      author={Yawar Siddiqui and Duncan Frost and Samir Aroudj and Armen Avetisyan and Henry Howard-Jenkins and Daniel DeTone and Pierre Moulon and Qirui Wu and Zhengqin Li and Julian Straub and Richard Newcombe and Jakob Engel},
      year={2026},
      eprint={2601.11514},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.11514}, 
}
```