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.

翻译：镜面反射在日常环境中普遍存在，且能在单次拍摄中提供立体信息，因为真实视角与反射形成的虚拟视角可同时被观测到。我们利用这一特性，将反射视为辅助视角，并设计了一种变换以构建物理有效的虚拟相机，从而能够在遵循真实成像过程的同时，直接在像素域生成虚拟视角。这使得从单张图像中构建多视角立体成像系统成为可能，简化了成像流程，并使其能够与强大的前馈式重建模型兼容，实现泛化性强且鲁棒的三维重建。为进一步利用镜面引入的几何对称性，我们提出了一种对称感知损失以优化姿态估计。我们的框架还可自然地扩展至动态场景，其中每帧图像均包含镜面反射，从而实现高效的逐帧几何恢复。为进行定量评估，我们提供了一个包含16个Blender场景的完全可定制合成数据集，每个场景均提供真实点云与相机位姿真值。通过在真实数据与合成数据上进行大量实验，验证了我们方法的有效性。