Large-Sparse-Reconstruction-Model

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