论文标题
神经隐式表面的球体指导训练
Sphere-Guided Training of Neural Implicit Surfaces
论文作者
论文摘要
近年来，通过体积射线游行训练的神经距离功能已被广泛用于多视图3D重建。但是，这些方法应用了整个场景量的射线行进程序，从而降低了采样效率，因此，高频细节领域的重建质量降低了。在这项工作中，我们通过对隐式功能的联合培训和我们新的基于粗球的表面重建来解决此问题。我们使用粗糙表示，有效地将场景的空体积从体积射线行进过程中排除，而没有神经表面网络的额外向前传递，这导致与基本系统相比，重建的忠诚度增加了。我们通过将其纳入几种隐式表面建模方法的训练程序中来评估我们的方法，并观察到合成和现实世界数据集的统一改进。可以通过项目页面访问我们的代码库：https：//andreeadogaru.github.io/sphereguided
In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marching procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower reconstruction quality in the areas of high-frequency details. In this work, we address this problem via joint training of the implicit function and our new coarse sphere-based surface reconstruction. We use the coarse representation to efficiently exclude the empty volume of the scene from the volumetric ray marching procedure without additional forward passes of the neural surface network, which leads to an increased fidelity of the reconstructions compared to the base systems. We evaluate our approach by incorporating it into the training procedures of several implicit surface modeling methods and observe uniform improvements across both synthetic and real-world datasets. Our codebase can be accessed via the project page: https://andreeadogaru.github.io/SphereGuided
