A data-driven reflectance model
A data-driven reflectance model
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DOI：
10.1145/882262.882343
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发表时间：
2003-07-01
影响因子：
6.2
通讯作者：
McMillan, L
中科院分区：
文献类型：
--
作者：
Matusik, W;Pfister, H;McMillan, L
我们提出了一个各向同性双向反射分布函数（BRDFs）的基础上获得的反射率数据的生成模型。而不是使用分析反射率模型，我们代表每个BRDF作为一个密集的测量集。这使我们能够在获得的BRDF空间中进行内插和外推，以创建新的BRDF。我们将每个获得的BRDF视为从所有可能的BRDF空间中获取的单个高维向量。我们应用线性（子空间）和非线性（流形）降维工具，努力发现一个低维的表示，我们的测量的特点。我们让用户定义感知上有意义的参数化方向，在降维BRDF空间中导航。在低维流形上，沿着这些方向的运动产生新颖但有效的BRDF。
We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space of acquired BRDFs to create new BRDFs. We treat each acquired BRDF as a single high-dimensional vector taken from a space of all possible BRDFs. We apply both linear (subspace) and non-linear (manifold) dimensionality reduction tools in an effort to discover a lower-dimensional representation that characterizes our measurements. We let users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space. On the low-dimensional manifold, movement along these directions produces novel but valid BRDFs.