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

# 3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image

[**Project Page**](https://zx-yin.github.io/3dfixer/) | [**Paper**](https://huggingface.co/papers/2604.04406) | [**GitHub**](https://github.com/HorizonRobotics/3D-Fixer)

3D-Fixer introduces a novel **In-Place Completion** paradigm to create high-fidelity 3D scenes from a single image. It extends 3D object generative priors to generate complete 3D assets conditioned on partially visible point clouds, using fragmented geometry as a spatial anchor to preserve layout fidelity without the need for time-consuming pose optimization.

## Sample Usage

The model can be loaded using the following code snippet from the [official repository](https://github.com/HorizonRobotics/3D-Fixer):

```python
# Load the pretrained model
ThreeDFixerPipeline.from_pretrained("HorizonRobotics/3D-Fixer")
```

## Dataset

The model was trained on **ARSG-110K**, a large-scale scene-level dataset comprising over 110K diverse scenes and 3M annotated images with high-fidelity 3D ground truth.

## Citation

```bibtex
@inproceedings{yin2026tdfixer,
  title={3D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Image},
  author={Yin, Ze-Xin and Liu, Liu and Wang, Xinjie and Sui, Wei and Su, Zhizhong and Yang, Jian and Xie, jin},
  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
  year={2026}
}
```