---
license: apache-2.0
pipeline_tag: image-to-3d
---

# AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting

[**Paper**](https://huggingface.co/papers/2605.10239) | [**Code**](https://github.com/xmw666/AdaptSplat)

AdaptSplat is a lightweight adapter design for feed-forward 3D Gaussian Splatting (3DGS). It introduces a single Frequency-Preserving Adapter (FPA) of only 1.5M parameters into a generic architecture to achieve superior performance in cross-domain generalization and high-frequency geometric fidelity. By extracting direction-aware high-frequency structural priors from a vision foundation model backbone (DINOv3-distilled ConvNeXt), it effectively compensates for high-frequency attenuation caused by over-smoothing in deep features.

## Inference

To run inference, please refer to the environment setup in the [official repository](https://github.com/xmw666/AdaptSplat). Once the environment and weights are prepared, you can run inference using the following commands:

### Single-GPU Inference
```bash
CUDA_VISIBLE_DEVICES=0 python inference.py --config configs/inference.yaml
```

### Multi-GPU (DDP) Inference
```bash
torchrun --nproc_per_node=8 inference_ddp.py --config configs/inference.yaml
```

## Citation

If you find this work useful, please cite:

```bibtex
@article{adaptsplat2026,
  title={AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting},
  author={Mingwei Xing, Xinliang Wang, Yifeng Shi},
  journal={arXiv preprint arXiv:2605.10239},
  year={2026}
}
```