  Existing NeRF-based inverse rendering methods suppose that scenes are
exclusively illuminated by distant light sources, neglecting the potential
influence of emissive sources within a scene. In this work, we confront this
limitation using LDR multi-view images captured with emissive sources turned on
and off. Two key issues must be addressed: 1) ambiguity arising from the
limited dynamic range along with unknown lighting details, and 2) the expensive
computational cost in volume rendering to backtrace the paths leading to final
object colors. We present a novel approach, ESR-NeRF, leveraging neural
networks as learnable functions to represent ray-traced fields. By training
networks to satisfy light transport segments, we regulate outgoing radiances,
progressively identifying emissive sources while being aware of reflection
areas. The results on scenes encompassing emissive sources with various
properties demonstrate the superiority of ESR-NeRF in qualitative and
quantitative ways. Our approach also extends its applicability to the scenes
devoid of emissive sources, achieving lower CD metrics on the DTU dataset.
