Multi-view surface reconstruction is an ill-posed, inverse problem in 3D vision research. It involves modeling the geometry and appearance with appropriate surface representations. Most of the existing methods rely either on explicit meshes, using surface rendering of meshes for reconstruction, or on implicit field functions, using volume rendering of the fields for reconstruction. The two types of representations in fact have their respective merits. In this work, we propose a new hybrid representation, termed Sur2f, aiming to better benefit from both representations in a complementary manner. Technically, we learn two parallel streams of an implicit signed distance field and an explicit surrogate surface Sur2f mesh, and unify volume rendering of the implicit signed distance function (SDF) and surface rendering of the surrogate mesh with a shared, neural shader; the unified shading promotes their convergence to the same, underlying surface. We synchronize learning of the surrogate mesh by driving its deformation with functions induced from the implicit SDF. In addition, the synchronized surrogate mesh enables surface-guided volume sampling, which greatly improves the sampling efficiency per ray in volume rendering. We conduct thorough experiments showing that Sur$^2$f outperforms existing reconstruction methods and surface representations, including hybrid ones, in terms of both recovery quality and recovery efficiency.

翻译：多视图表面重建是三维视觉研究中一个病态的逆问题，它涉及使用合适的表面表示对几何形状与外观进行建模。现有方法大多依赖于显式网格（利用网格的表面渲染进行重建）或隐式场函数（利用场的体渲染进行重建）。这两种表示实际上各有优点。本文提出一种新的混合表示方法，称为Sur2f，旨在以互补方式更好地利用这两种表示。技术上，我们并行学习隐式有符号距离场与显式代理表面Sur2f网格两个流，并通过共享的神经着色器统一隐式有符号距离函数（SDF）的体渲染与代理网格的表面渲染；这种统一着色促使两者收敛至同一底层表面。我们通过由隐式SDF诱导的函数驱动代理网格的变形，从而同步其学习过程。此外，同步的代理网格实现了表面引导的体采样，极大提高了体渲染中每条光线的采样效率。充分的实验表明，Sur²f在恢复质量与恢复效率方面均优于现有重建方法及包括混合表示在内的表面表示方法。