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。

利用多个先验条件进行神经网络 3D 室内重建

Exploiting Multiple Priors for Neural 3D Indoor Reconstruction

Reconstructing high-quality 3D objects from sparse, partial observations from
a single view is of crucial importance for various applications in computer
vision, robotics, and graphics. While recent neural implicit modeling methods
show promising results on synthetic or dense data, they perform poorly on
sparse and noisy real-world data. We discover that the limitations of a popular
neural implicit model are due to lack of robust shape priors and lack of proper
regularization. In this work, we demonstrate highquality in-the-wild shape
reconstruction using: (i) a deep encoder as a robust-initializer of the shape
latent-code; (ii) regularized test-time optimization of the latent-code; (iii)
a deep discriminator as a learned high-dimensional shape prior; (iv) a novel
curriculum learning strategy that allows the model to learn shape priors on
synthetic data and smoothly transfer them to sparse real world data. Our
approach better captures the global structure, performs well on occluded and
sparse observations, and registers well with the ground-truth shape. We
demonstrate superior performance over state-of-the-art 3D object reconstruction
methods on two real-world datasets.

本文提出了一种基于神经隐式模型的 3D 物体重建方法，通过深度编码器作为初始形状潜码的鲁棒优化器，利用规则化测试时间优化潜码，深度鉴别器作为高维形状先验，并使用新颖的课程学习策略，从而更好地捕捉全局细节，在模糊的基准数据集上表现出色，展示了在真实世界数据上超越现有技术的优越性。