  In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene
reconstruction and editing using differentiable Monte Carlo raytracing on
neural signed distance fields (SDFs). Our holistic neural SDF-based framework
jointly recovers the underlying shapes, incident radiance and materials from
multi-view images. We introduce a novel bubble loss for fine-grained small
objects and error-guided adaptive sampling scheme to largely improve the
reconstruction quality on large-scale indoor scenes. Further, we propose to
decompose the neural radiance field into spatially-varying material of the
scene as a neural field through surface-based, differentiable Monte Carlo
raytracing and emitter semantic segmentations, which enables physically based
and photorealistic scene relighting and editing applications. Through a number
of qualitative and quantitative experiments, we demonstrate the superior
quality of our method on indoor scene reconstruction, novel view synthesis, and
scene editing compared to state-of-the-art baselines.
