---
license: apache-2.0
pipeline_tag: image-to-3d
tags:
- dino
- scene-understanding
- semantic-scene-completion
- unsupervised
library_name: pytorch
---
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
[**Aleksandar Jevtić**](https://jev-aleks.github.io/)
*1
[**Christoph Reich**](https://christophreich1996.github.io/)
*1,2,4,5
[**Felix Wimbauer**](https://fwmb.github.io/)
1,4
[**Oliver Hahn**](https://olvrhhn.github.io/)
2
[**Christian Rupprecht**](https://chrirupp.github.io/)
3
[**Stefan Roth**](https://www.visinf.tu-darmstadt.de/visual_inference/people_vi/stefan_roth.en.jsp)
2,5,6
[**Daniel Cremers**](https://cvg.cit.tum.de/members/cremers/)
1,4,5
1TU Munich
2TU Darmstadt
3University of Oxford
4MCML
5ELIZA
6hessian.AI *equal contribution

[](https://pytorch.org/)
## Overview
SceneDINO is unsupervised and infers 3D geometry and features from a single image in a feed-forward manner. Distilling and clustering SceneDINO's 3D feature field results in unsupervised semantic scene completion predictions. The method is trained using multi-view self-supervision.
## Installation & Quick Start
Please refer to our [Github Repo](https://github.com/tum-vision/scenedino).
## Citation
If you find our work useful, please consider giving it a star ⭐ and citing our paper.
```bibtex
@inproceedings{Jevtic:2025:SceneDINO,
author = {Aleksandar Jevti{\'c} and
Christoph Reich and
Felix Wimbauer and
Oliver Hahn and
Christian Rupprecht and
Stefan Roth and
Daniel Cremers},
title = {Feed-Forward {SceneDINO} for Unsupervised Semantic Scene Completion},
journal = {IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2025},
}
```