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.

翻译：本文提出I$^2$-SDF方法，一种利用可微分蒙特卡洛光线追踪在神经有符号距离函数（SDFs）上实现室内场景内禀重建与编辑的新技术。该基于神经SDF的整体框架可从多视角图像中联合恢复底层几何形状、入射辐射度和材质。我们引入新型气泡损失函数以处理精细小物体，并提出误差引导的自适应采样策略，大幅提升大规模室内场景的重建质量。进一步，我们提出通过基于表面的可微分蒙特卡洛光线追踪和发射体语义分割，将神经辐射场分解为场景的空间变化材质神经场，从而实现基于物理的光照真实感场景重光照与编辑应用。通过大量定性与定量实验，我们证明该方法在室内场景重建、新视角合成和场景编辑方面的质量均优于当前最先进的基线方法。