SHINOBI: Shape and Illumination using Neural Object Decomposition via
  BRDF Optimization In-the-wild
Authors: name: Andreas Engelhardt, name: Amit Raj, name: Mark Boss, name: Yunzhi Zhang, name: Abhishek Kar, name: Yuanzhen Li, name: Deqing Sun, name: Ricardo Martin Brualla, name: Jonathan T. Barron, name: Hendrik P. A. Lensch, name: Varun Jampani

We present SHINOBI, an end-to-end framework for the reconstruction of shape,
material, and illumination from object images captured with varying lighting,
pose, and background. Inverse rendering of an object based on unconstrained
image collections is a long-standing challenge in computer vision and graphics
and requires a joint optimization over shape, radiance, and pose. We show that
an implicit shape representation based on a multi-resolution hash encoding
enables faster and robust shape reconstruction with joint camera alignment
optimization that outperforms prior work. Further, to enable the editing of
illumination and object reflectance (i.e. material) we jointly optimize BRDF
and illumination together with the object's shape. Our method is class-agnostic
and works on in-the-wild image collections of objects to produce relightable 3D
assets for several use cases such as AR/VR, movies, games, etc. Project page:
https://shinobi.aengelhardt.com Video:
https://www.youtube.com/watch?v=iFENQ6AcYd8&feature=youtu.be