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.

翻译：神经隐式建模在小物体上能够实现令人印象深刻的三维重建效果，但在大规模室内场景中表现存在显著局限性。本文提出了一种新颖的神经隐式建模方法，该方法利用多种正则化策略，仅依赖图像即可实现大型室内环境的更优重建。稀疏而精确的深度先验被用于将场景锚定至初始模型。同时引入密集但精度较低的深度先验，该先验具有足够的灵活性，仍允许模型偏离其约束以改进几何估计。此外，本文提出了一种新颖的自监督策略来正则化估计的表面法向量。最后，可学习曝光补偿方案使模型能够应对具有挑战性的光照条件。实验结果表明，我们的方法在具有挑战性的室内场景中实现了最先进的三维重建效果。