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

翻译：近年来，通过体积射线行进训练的神经距离函数已被广泛用于多视图三维重建。然而，这些方法对整个场景体积应用射线行进过程，导致采样效率降低，从而在高频细节区域出现重建质量下降。本文中，我们通过联合训练隐式函数与新型粗粒度球体曲面重建来解决这一问题。我们利用粗分辨率表示高效地从体积射线行进过程中排除场景的空体积区域，无需对神经曲面网络进行额外前向传播，从而相较于基础系统提升重建保真度。我们将该方法集成到多种隐式曲面建模方法的训练流程中进行评估，观察到在合成数据集和真实世界数据集上均取得一致性能提升。我们的代码库可通过项目页面访问：https://andreeadogaru.github.io/SphereGuided