Reflect3r
Single-View 3D Stereo Reconstruction Aided by Mirror Reflections
3DV 2026
Jing Wu Zirui Wang Iro Laina Victor Adrian Prisacariu
University of Oxford
Citation
@article{wu2026reflect3r,
author = {Wu, Jing and Wang, Zirui and Laina, Iro and Prisacariu, Victor},
title = {{Reflect3r: Single-View 3D Stereo Reconstruction Aided by Mirror Reflections}},
journal = {3DV},
year = {2026},
}
Abstract
Mirror reflections are common in everyday environments and can provide stereo information within a single capture, as the real and reflected virtual views are visible simultaneously. We exploit this property by treating the reflection as an auxiliary view and designing a transformation that constructs a physically valid virtual camera, allowing direct pixel-domain generation of the virtual view while adhering to the real-world imaging process. This enables a multi-view stereo setup from a single image, simplifying the imaging process, making it compatible with powerful feed-forward reconstruction models for generalizable and robust 3D reconstruction. To further exploit the geometric symmetry introduced by mirrors, we propose a symmetric-aware loss to refine pose estimation. Our framework also naturally extends to dynamic scenes, where each frame contains a mirror reflection, enabling efficient per-frame geometry recovery. For quantitative evaluation, we provide a fully customizable synthetic dataset of 16 Blender scenes, each with ground-truth point clouds and camera poses. Extensive experiments on real-world data and synthetic data are conducted to illustrate the effectiveness of our method.
Method
Reflect3r reconstructs 3D scenes from a single-view image by leveraging mirror reflections. A reflection transformation is designed to ensure that flipping the real view in the pixel domain, simulating a virtual camera imaging, enables seamless integration with modern feed-forward models. Following the initial prediction, the reflection transformation is used as a geometric constraint to refine pose optimization.
Synthetic Blender Data 
Thumbnails of the dataset. where each image represents a fully customizable Blender scene.
Download
🤗 HuggingFace Data- Download the original editable Blender scenes (
*.blend) from here. - Download the rendered and processed data (ground-truth point clouds) from here.
- Check here and here for more details about our data.
Preview
We visualize some examples of the synthetic ground-truth point clouds below for preview.
Use the controls to switch between examples.
Results
We show the reconstruction results on the synthetic data.
Acknowledgements
Our Code is built upon:
DUSt3R
The 16 Blender scenes are collected from online websites, we rearranged and cleaned the scenes and modelled a mirror on top of them for research purpose. Here we listed all the original download links for these scenes, we thank these designers for their great work.