| 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} | |
| } | |
| ``` |