  Physics-based inverse rendering enables joint optimization of shape,
material, and lighting based on captured 2D images. To ensure accurate
reconstruction, using a light model that closely resembles the captured
environment is essential. Although the widely adopted distant environmental
lighting model is adequate in many cases, we demonstrate that its inability to
capture spatially varying illumination can lead to inaccurate reconstructions
in many real-world inverse rendering scenarios. To address this limitation, we
incorporate NeRF as a non-distant environment emitter into the inverse
rendering pipeline. Additionally, we introduce an emitter importance sampling
technique for NeRF to reduce the rendering variance. Through comparisons on
both real and synthetic datasets, our results demonstrate that our NeRF-based
emitter offers a more precise representation of scene lighting, thereby
improving the accuracy of inverse rendering.
