Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images
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.
Dream-to-Recon comprises three steps: a) We train a view completion model (VCM) that inpaints occluded areas and refines warped views. Training uses only a single view per scene and leverages forward-backward warping for data generation. b) The VCM is applied iteratively alongside a depth prediction network to synthesize virtual novel views, enabling progressive refinement of the 3D geometry. c) The synthesized scene geometries are then used to distill a feed-forward scene reconstruction model by supervising occupancy and virtual depth.
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@inproceedings{wulff2025dreamtorecon,
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 = {IEEE International Conference on Computer Vision (ICCV)},
year = {2025}
}
Acknowledgements: This work was funded by the ERC Advanced Grant ”SIMULACRON” (agreement #884679), the GNI Project ”AI4Twinning”, and the DFG project CR 250/26-1 ”4D YouTube”.