Reflect3r: Single-View 3D Stereo Reconstruction Aided by Mirror Reflections

Jing Wu, Zirui Wang, Iro Laina, Victor Adrian Prisacariu

Keywords: Single-view 3D reconstruction, Mirror, Reflections

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 flexible, robust, and accurate 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.
Code and Blender scenes are available in: https://github.com/jingwu2121/reflect3r.git.

Submission Number: 30