IEEE Trans Pattern Anal Mach Intell. 2023 Apr;45(4):4355-4367. doi: 10.1109/TPAMI.2022.3198729. Epub 2023 Mar 7.
We consider the problem of estimating surface normals of a scene with spatially varying, general bidirectional reflectance distribution functions (BRDFs) observed by a static camera under varying distant illuminations. Unlike previous approaches that rely on continuous optimization of surface normals, we cast the problem as a discrete search problem over a set of finely discretized surface normals. In this setting, we show that the expensive processes can be precomputed in a scene-independent manner, resulting in accelerated inference. We discuss two variants of our discrete search photometric stereo (DSPS), one working with continuous linear combinations of BRDF bases and the other working with discrete BRDFs sampled from a BRDF space. Experiments show that DSPS has comparable accuracy to state-of-the-art exemplar-based photometric stereo methods while achieving 10-100x acceleration.
我们考虑了在静态相机下，由不同距离照明观察到的具有空间变化的、一般双向反射分布函数（BRDF）的场景的表面法线估计问题。与以前依赖于表面法线连续优化的方法不同，我们将问题表述为在一组精细离散化的表面法线上进行离散搜索问题。在这种情况下，我们表明昂贵的过程可以以与场景无关的方式预先计算，从而实现加速推理。我们讨论了我们的离散搜索摄影测量（DSPS）的两个变体，一个变体使用 BRDF 基的连续线性组合，另一个变体使用从 BRDF 空间中采样的离散 BRDF。实验表明，DSPS 在实现 10-100 倍加速的同时，与基于范例的最新摄影测量方法具有相当的准确性。
