Add model card

README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+- computer-vision
+- 3d-gaussian-splatting
+- gaussian-splatting
+- super-resolution
+- eccv-2026
+- pytorch
+pipeline_tag: image-to-3d
+---
+
+# AnchorSplat 20x Checkpoint
+
+This repository hosts the released 20x checkpoint for **AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting (ECCV 2026)**.
+
+- Paper: https://arxiv.org/abs/2607.01290
+- Code: https://github.com/zhude233/AnchorSplat
+- Checkpoint: `anchorsplat_20x.pth`
+- SHA256: `ad05f8b965c002c1f62cea53e4ce10ed4804bbc433375afa5f411f236d1b79a3`
+
+## Usage
+
+Download the checkpoint and place it at:
+
+```text
+checkpoints/anchorsplat_20x.pth
+```
+
+or pass it explicitly:
+
+```bash
+WEIGHTS=/path/to/anchorsplat_20x.pth \
+  bash scripts/inference_external.sh examples/lgm_sample.ply outputs/lgm_sample_refined.ply lgm
+```
+
+## Notes
+
+AnchorSplat is designed for fast, generalizable, plug-and-play enhancement of low-quality 3D Gaussian Splatting assets. For external PLY inputs, please follow the input format and normalization instructions in the GitHub README.
+
+## Citation
+
+```bibtex
+@article{zhu2026anchorsplat,
+  title={AnchorSplat: Fast and Structure Consistent Detail Synthesis for Gaussian Splatting},
+  author={Zhu, Dexu and Shao, Jiangnan and Wang, Xiaofeng and Duan, Junxian and Cao, Jie and Zhu, Zheng and Huang, Huaibo},
+  journal={arXiv preprint arXiv:2607.01290},
+  year={2026},
+  eprint={2607.01290},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  doi={10.48550/arXiv.2607.01290},
+  url={https://arxiv.org/abs/2607.01290}
+}
+```