In recent years, neural implicit surface reconstruction methods have become popular for multi-view 3D reconstruction. In contrast to traditional multi-view stereo methods, these approaches tend to produce smoother and more complete reconstructions due to the inductive smoothness bias of neural networks. State-of-the-art neural implicit methods allow for high-quality reconstructions of simple scenes from many input views. Yet, their performance drops significantly for larger and more complex scenes and scenes captured from sparse viewpoints. This is caused primarily by the inherent ambiguity in the RGB reconstruction loss that does not provide enough constraints, in particular in less-observed and textureless areas. Motivated by recent advances in the area of monocular geometry prediction, we systematically explore the utility these cues provide for improving neural implicit surface reconstruction. We demonstrate that depth and normal cues, predicted by general-purpose monocular estimators, significantly improve reconstruction quality and optimization time. Further, we analyse and investigate multiple design choices for representing neural implicit surfaces, ranging from monolithic MLP models over single-grid to multi-resolution grid representations. We observe that geometric monocular priors improve performance both for small-scale single-object as well as large-scale multi-object scenes, independent of the choice of representation.
翻译:近些年来,神经隐含表面重建方法在多视角3D重建中变得十分流行。与传统的多视角立体法不同,这些方法往往由于神经网络的感性平稳偏差而产生更顺利和更完整的重建。最先进的神经隐含方法使得从许多投入观点中可以对简单场景进行高质量的重建。然而,它们的表现对于规模更大、更复杂的场景和从稀少的视野中捕捉到的场景来说显著下降。这主要是由于RGB重建损失的内在模糊性没有提供足够的限制,特别是在观测较少和没有纹理的地区。这些方法与传统的多视角立体立体的立体方法不同。由于在单向几何几何几何几何测深的预测领域取得的最新进展,我们系统地探索了这些线索对于改善神经隐含表面重建的效用。我们展示了一般目的单向估计的深度和正常信号,大大提高了重建质量和优化时间。此外,我们分析并调查了代表神经隐含表面的多种设计选择,从单电网上的单立MLP模型到多分辨率的网格图示图。我们观察观察了地表上前一幅独立的、前一幅独立的多度的多元单向的图像。