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arXiv · 2508.02323

Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images

Philipp Wulff·Felix Wimbauer·Dominik Muhle·Daniel Cremers
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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.

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Philipp Wulff, Felix Wimbauer, Dominik Muhle, Daniel Cremers. 2025-08-04. Dream-to-Recon: Monocular 3D Reconstruction with Diffusion-Depth Distillation from Single Images. https://arxiv.org/abs/2508.02323

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