SAIL Recon

Citation [optional]

BibTeX:

If you find this project useful in your research, please consider citing:

@article{dengli2025sail,
  title={SAIL-Recon: Large SfM by Augmenting Scene Regression with Localization},
  author={Deng, Junyuan and Li, Heng and Xie, Tao and Ren, Weiqiang and Zhang, Qian and Tan, Ping and Guo, Xiaoyang},
  journal={arXiv preprint arXiv:2508.17972},
  year={2025}
}

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards tags: - image-to-3d --- # SAIL-Recon <!-- Provide a quick summary of what the model is/does. --> Sail-Recon is a feed-forward Transformer that scales neural scene regression to large-scale Structure-from-Motion by augmenting it with visual localization. From a few anchor views, it constructs a global latent scene representation that encodes both geometry and appearance. Conditioned on this representation, the network directly regresses camera poses, intrinsics, depth maps, and scene coordinate maps for thousands of images in minutes, enabling precise and robust reconstruction without iterative optimization. ## Model Details ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [https://github.com/HKUST-SAIL/sail-recon](https://github.com/HKUST-SAIL/sail-recon) - **Paper [optional]:** [https://github.com/HKUST-SAIL/sail-recon](https://huggingface.co/papers/2508.17972) - **Demo [optional]:** [https://huggingface.co/spaces/HKUST-SAIL/SAIL-Recon](https://huggingface.co/spaces/HKUST-SAIL/SAIL-Recon) ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** If you find this project useful in your research, please consider citing: ```bibtex @article{dengli2025sail, title={SAIL-Recon: Large SfM by Augmenting Scene Regression with Localization}, author={Deng, Junyuan and Li, Heng and Xie, Tao and Ren, Weiqiang and Zhang, Qian and Tan, Ping and Guo, Xiaoyang}, journal={arXiv preprint arXiv:2508.17972}, year={2025} } ```