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# LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

![Teaser](teaser.png)

Official model release for the paper
**LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows**. LSRM is a feed-forward, object-centric 3D reconstruction and inverse rendering model that reconstructs high-fidelity 3D assets from posed sparse multi-view images. The model is trained on synthetic data but comprehensively tested on real data.

* Project page: <https://lzqsd.github.io/LSRM.github.io/>
* arXiv: <https://arxiv.org/abs/2604.05182>
* Github: <https://github.com/facebookresearch/Large-Sparse-Reconstruction-Model.git>

## Quickstart

This Hugging Face repository hosts only the **pre-trained weights and test
examples**. To run inference, clone the GitHub repo and follow its README:

```sh
git clone https://github.com/facebookresearch/Large-Sparse-Reconstruction-Model.git
cd Large-Sparse-Reconstruction-Model
# then follow README.md for setup and test commands
```

## Checkpoints and testing data

This repo hosts pre-trained weights and testing examples.

### Checkpoints

Our `checkpoints/` folder contains model weights for our 3D reconstruction
and inverse rendering models, with the following layout (this is what the
test scripts expect):

```
checkpoints/
├── rgb/                       # 3D reconstruction (GSO)
│   ├── dense/
│   │   ├── args.txt
│   │   └── checkpoints/last.pth
│   └── sparse.pth
└── brdf/                      # inverse rendering (ORB / DTC)
    ├── dense/
    │   ├── args.txt
    │   └── checkpoints/last.pth
    └── sparse.pth
```

### Testing data

This repo also provides one testing example from each of the GSO, DTC, and
ORB datasets, plus the default HDR environment map `env.exr`.

```
datasets/
├── rgb/
│   └── gso_example/           # GSO example (3D reconstruction)
└── brdf/
    ├── orb_example/           # ORB example (inverse rendering)
    └── dtc_example/           # DTC example (inverse rendering)

env.exr                       # default HDR environment map (repo root)
```

`env.exr` is the default HDR environment map used by Blender for re-rendering
predicted BRDFs / meshes. The test scripts pick it up automatically; you only
need to replace it if you want to re-light results under a different
illumination.

### Dataset format

To run on your own captures, mirror one of the example layouts. The two
formats are:

**RGB (GSO-style, for `test_rgb.sh`)**

Loaded by `data_loader/dtc_dataset.py`. Assumes the object is inside a
bounding sphere of radius **0.25**, which is scaled to **0.5** during data
loading (matching the `--bbox_radius 0.5` used in the test scripts).

```
datasets/rgb/<your_split>/
├── test.txt                     # one scene name per line
└── <scene_name>/
    ├── images/
    │   ├── CameraRig.json
    │   ├── image_process_info.json
    │   └── rgb/
    │       ├── rgb0000000.png … rgb000NNNN.png      # input views
    │       └── mask0000000.png … mask000NNNN.png    # foreground masks
    └── scene/
        ├── aria_trajectory.csv
        └── scene_info.json
```

**BRDF (ORB / DTC-style, for `test_brdf.sh`)**

Loaded by `data_loader/real_dataset.py`. Assumes the object is inside a
bounding sphere of radius **0.5** (matching the `--bbox_radius 0.5` used in
the test scripts).

```
datasets/brdf/<your_split>/
├── test.txt                     # one scene name per line
└── <scene_name>/
    ├── transforms_input.json    # camera poses for the input views
    ├── transforms_output.json   # camera poses for the eval views
    ├── scale_center.txt         # per-scene scene normalization
    ├── input/                   # input RGB
    ├── mask/                    # input foreground masks
    ├── output/                  # eval-view RGB (ground truth)
    ├── mask_output/             # eval-view foreground masks
    └── env/                     # environment maps (per scene)
```

To use the GitHub code on your own captures, mirror one of the example
layouts and point the corresponding test script at your split by editing
`data_path` inside the `.sh` file (or by overriding it on the command line).

## License

This project is licensed under the Creative Commons Attribution-NonCommercial
4.0 International (CC BY-NC 4.0) license. See [LICENSE.md](LICENSE.md) for
details.

## Acknowledgements

Our Triton implementation of native sparse attention builds on
[lucidrains/native-sparse-attention-pytorch](https://github.com/lucidrains/native-sparse-attention-pytorch)
and draws inspiration from
[DreamTechAI/Direct3D-S2](https://github.com/DreamTechAI/Direct3D-S2). We thank
the authors of both projects for releasing their work.

## Citation

If you find this work useful, please cite:

```bibtex
@article{lsrm,
    title   = {LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows},
    author  = {Zhengqin Li and Cheng Zhang and Jakob Engel and Zhao Dong},
    journal = {arXiv preprint arXiv:2604.05182},
    year    = {2026}
}
```