Neural implicit modeling permits to achieve impressive 3D reconstruction
results on small objects, while it exhibits significant limitations in large
indoor scenes. In this work, we propose a novel neural implicit modeling method
that leverages multiple regularization strategies to achieve better
reconstructions of large indoor environments, while relying only on images. A
sparse but accurate depth prior is used to anchor the scene to the initial
model. A dense but less accurate depth prior is also introduced, flexible
enough to still let the model diverge from it to improve the estimated
geometry. Then, a novel self-supervised strategy to regularize the estimated
surface normals is presented. Finally, a learnable exposure compensation scheme
permits to cope with challenging lighting conditions. Experimental results show
that our approach produces state-of-the-art 3D reconstructions in challenging
indoor scenarios.

我们提出了一种新颖的神经隐式建模方法，利用多种正则化策略在仅依赖图像的情况下实现大型室内环境的更好重建，通过稀疏但准确的深度先验将场景锚定到初始模型，并引入一种密集但精度较低的深度先验，灵活性足以让模型从中分离出来改善估计的几何建模，并提出了一种新颖的自监督策略来规范估计的表面法线，最后，可学习的曝光补偿方案适应复杂的光照条件。实验结果表明，我们的方法在具有挑战性的室内场景中产生了最先进的 3D 重建结果。