In order to enable view-dependent appearance synthesis from the light fields of a scene, it is critical to evaluate the geometric relationships between light and view over surfaces in the scene with high accuracy. Perfect diffuse reflectance is commonly assumed to estimate geometry from light fields via multiview stereo. However, this diffuse surface assumption is invalid with real-world objects. Geometry estimated from light fields is severely degraded over specular surfaces. Additional scene-scale 3D scanning based on active illumination could provide reliable geometry, but it is sparse and thus still insufficient to calculate view-dependent appearance, such as specular reflection, in geometry-based view synthesis. In this work, we present a practical solution of inverse rendering to enable view-dependent appearance synthesis, particularly of scene scale. We enhance the scene geometry by eliminating the specular component, thus enforcing photometric consistency. We then estimate spatially-varying parameters of diffuse, specular, and normal components from wide-baseline light fields. To validate our method, we built a wide-baseline light field imaging prototype that consists of 32 machine vision cameras with fisheye lenses of 185 degrees that cover the forward hemispherical appearance of scenes. We captured various indoor scenes, and results validate that our method can estimate scene geometry and reflectance parameters with high accuracy, enabling view-dependent appearance synthesis at scene scale with high fidelity, i.e., specular reflection changes according to a virtual viewpoint.

  @InProceedings{SceneViewSynthesis:ICCP:2021,
  author  = {Dahyun Kang and Daniel S. Jeon and Hakyeong Kim and
             Hyeonjoong Jang and Min H. Kim},
  title   = {View-dependent Scene Appearance Synthesis using
             Inverse Rendering from Light Fields},
  booktitle = {Proc. IEEE International Conference on
               Computational Photography (ICCP) 2021)},
  year    = {2021},
  month = {May},
  }