We propose a method for estimating high-definition spatially-varying
lighting, reflectance, and geometry of a scene from 360$^{\circ}$ stereo
images. Our model takes advantage of the 360$^{\circ}$ input to observe the
entire scene with geometric detail, then jointly estimates the scene's
properties with physical constraints. We first reconstruct a near-field
environment light for predicting the lighting at any 3D location within the
scene. Then we present a deep learning model that leverages the stereo
information to infer the reflectance and surface normal. Lastly, we incorporate
the physical constraints between lighting and geometry to refine the
reflectance of the scene. Both quantitative and qualitative experiments show
that our method, benefiting from the 360$^{\circ}$ observation of the scene,
outperforms prior state-of-the-art methods and enables more augmented reality
applications such as mirror-objects insertion.

本文提出了一种从 360 度立体图像中估计场景高清空间变化光照、反射和几何的方法，该方法利用全景图像获得场景的详细几何信息，结合物理约束共同估计场景属性。通过重建场景的近场光环境、运用深度学习模型推断反射率和表面法线，加入光照与几何之间的物理约束以改进场景的反射率，实验结果表明该方法在增强现实应用中具有 State of the art 的性能有优势。