| --- |
| 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} |
| } |
| ``` |
|
|
