Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images
Philipp Wulff, Felix Wimbauer, Dominik Muhle, Daniel Cremers
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.
BibTeX
@InProceedings{Wulff_2025_ICCV,
author = {Wulff, Philipp and Wimbauer, Felix and Muhle, Dominik and Cremers, Daniel},
title = {Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {9352-9362}
}