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 | Paper | Code | Video | HF-Model | HF Evaluation Dataset
Clone the repository and follow the INSTALL.md instructions to install the required dependencies.
To run inference on a sample from the dataset:
python infer_shape.py --input_pkl <sample.pkl> --config balance --output_dir output
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.
ShapeR Evaluation Dataset 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 notebook which includes:
ShapeR evaluation dataset is licensed under CC-BY-NC. See LICENSE for details.
If you find ShapeR useful for your research, please cite our paper:
@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},
}
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 | Paper | Code | Video | HF-Model | HF Evaluation Dataset
Clone the repository and follow the INSTALL.md instructions to install the required dependencies.
To run inference on a sample from the dataset:
python infer_shape.py --input_pkl <sample.pkl> --config balance --output_dir output
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.
ShapeR Evaluation Dataset 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 notebook which includes:
ShapeR evaluation dataset is licensed under CC-BY-NC. See LICENSE for details.
If you find ShapeR useful for your research, please cite our paper:
@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},
}