  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.
