Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images
Abstract
Volumetric scene reconstruction from a single image is crucial for a broad range of applications like autonomous driving and robotics. Recent volumetric reconstruction methods achieve impressive results, but generally require expensive 3D ground truth or multi-view supervision. We propose to leverage pre-trained 2D diffusion models and depth prediction models to generate synthetic scene geometry from a single image. This can then be used to distill a feed-forward scene reconstruction model. Our experiments on the challenging KITTI-360 and Waymo datasets demonstrate that our method matches or outperforms state-of-the-art baselines that use multi-view supervision, and offers unique advantages, for example regarding dynamic scenes.
Cite
Text
Wulff et al. "Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images." International Conference on Computer Vision, 2025.Markdown
[Wulff et al. "Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images." International Conference on Computer Vision, 2025.](https://mlanthology.org/iccv/2025/wulff2025iccv-dreamtorecon/)BibTeX
@inproceedings{wulff2025iccv-dreamtorecon,
title = {{Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images}},
author = {Wulff, Philipp and Wimbauer, Felix and Muhle, Dominik and Cremers, Daniel},
booktitle = {International Conference on Computer Vision},
year = {2025},
pages = {9352-9362},
url = {https://mlanthology.org/iccv/2025/wulff2025iccv-dreamtorecon/}
}