  Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis
quality using coordinate-based neural scene representations. However, NeRF's
view dependency can only handle simple reflections like highlights but cannot
deal with complex reflections such as those from glass and mirrors. In these
scenarios, NeRF models the virtual image as real geometries which leads to
inaccurate depth estimation, and produces blurry renderings when the multi-view
consistency is violated as the reflected objects may only be seen under some of
the viewpoints. To overcome these issues, we introduce NeRFReN, which is built
upon NeRF to model scenes with reflections. Specifically, we propose to split a
scene into transmitted and reflected components, and model the two components
with separate neural radiance fields. Considering that this decomposition is
highly under-constrained, we exploit geometric priors and apply
carefully-designed training strategies to achieve reasonable decomposition
results. Experiments on various self-captured scenes show that our method
achieves high-quality novel view synthesis and physically sound depth
estimation results while enabling scene editing applications.
