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：在本文中，我们提出I$^2$-SDF——一种利用可微分蒙特卡洛光线追踪于神经有符号距离场（SDF）的全新方法，用于本征室内场景重建与编辑。基于整体神经SDF的框架可从多视角图像中联合恢复底层几何形状、入射辐射度与材质。我们引入新颖的泡状损失函数以处理精细小尺度物体，并结合误差引导的自适应采样方案，显著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与光源语义分割，将神经辐射场分解为场景的空间变化材质（以神经场形式表示），从而支持基于物理的逼真场景重光照与编辑应用。通过大量定性与定量实验，我们证明了该方法在室内场景重建、新视角合成及场景编辑任务中相较于现有最优方法的卓越性能。