  The goal of inverse rendering is to decompose geometry, lights, and materials
given pose multi-view images. To achieve this goal, we propose neural direct
and joint inverse rendering, NDJIR. Different from prior works which relies on
some approximations of the rendering equation, NDJIR directly addresses the
integrals in the rendering equation and jointly decomposes geometry: signed
distance function, lights: environment and implicit lights, materials: base
color, roughness, specular reflectance using the powerful and flexible volume
rendering framework, voxel grid feature, and Bayesian prior. Our method
directly uses the physically-based rendering, so we can seamlessly export an
extracted mesh with materials to DCC tools and show material conversion
examples. We perform intensive experiments to show that our proposed method can
decompose semantically well for real object in photogrammetric setting and what
factors contribute towards accurate inverse rendering.
