Recovering the 3D geometry of a purely texture-less object with generally
unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging
task in multi-view reconstruction. The major obstacle revolves around
establishing cross-view correspondences where photometric constancy is
violated. This paper proposes a simple and practical solution to overcome this
challenge based on a co-located camera-light scanner device. Unlike existing
solutions, we do not explicitly solve for correspondence. Instead, we argue the
problem is generally well-posed by multi-view geometrical and photometric
constraints, and can be solved from a small number of input views. We formulate
the reconstruction task as a joint energy minimization over the surface
geometry and reflectance. Despite this energy is highly non-convex, we develop
an optimization algorithm that robustly recovers globally optimal shape and
reflectance even from a random initialization. Extensive experiments on both
simulated and real data have validated our method, and possible future
extensions are discussed.

本文提出了一种简单且实用的解决方案，在多视角重建中使用基于联合几何和光度约束的能量最小化，利用一个共置的摄像机 - 光线扫描仪设备来克服建立跨视觉对应的问题。我们的优化算法可以从随机初始化中稳健地全局最优地恢复形状和反射率。实验结果验证了我们方法的有效性并讨论了未来的可能扩展。