TraM-NeRF: Tracing Mirror and Near-Perfect Specular Reflections through
  Neural Radiance Fields
Leif Van Holland Ruben Bliersbach Jan U. Müller Patrick Stotko Reinhard Klein
Implicit representations like Neural Radiance Fields (NeRF) showed impressive
results for photorealistic rendering of complex scenes with fine details.
However, ideal or near-perfectly specular reflecting objects such as mirrors,
which are often encountered in various indoor scenes, impose ambiguities and
inconsistencies in the representation of the reconstructed scene leading to
severe artifacts in the synthesized renderings. In this paper, we present a
novel reflection tracing method tailored for the involved volume rendering
within NeRF that takes these mirror-like objects into account while avoiding
the cost of straightforward but expensive extensions through standard path
tracing. By explicitly modeling the reflection behavior using physically
plausible materials and estimating the reflected radiance with Monte-Carlo
methods within the volume rendering formulation, we derive efficient strategies
for importance sampling and the transmittance computation along rays from only
few samples. We show that our novel method enables the training of consistent
representations of such challenging scenes and achieves superior results in
comparison to previous state-of-the-art approaches.