Sparse-as-possible SVBRDF acquisition
Sparse-as-possible SVBRDF acquisition
复制标题
尽可能稀疏的 SVBRDF 采集
DOI：
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发表时间：
2016
影响因子：
6.2
通讯作者：
Xin Tong
中科院分区：
文献类型：
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作者：
Zhiming Zhou;Guojun Chen;Yue Dong;D. Wipf;Yong Yu;John M. Snyder;Xin Tong
我们提出了一种新的方法来捕捉现实世界中，空间变化的表面反射从少量的对象视图（k）。我们的关键观察是，特定目标的反射率可以由少量的定制基础材料（N）表示，这些材料在每个点（n）处由甚至更少数量的非零权重凸混合。基于这种稀疏基/稀疏混合模型，我们开发了一种SVBRDF重建算法，该算法通过交替迭代优化来联合求解n，N，基BRDF及其空间混合权重，每一步都解决了线性约束二次规划问题。我们开发了一个数值工具，让我们估计所需的视图数量，并分析照明和几何重建质量的影响。我们验证了我们的方法与合成BRDF渲染的图像，并展示了令人信服的结果预扫描的形状和不受控制的自然照明照亮的真实的对象，从很少，甚至是一个单一的输入图像。
We present a novel method for capturing real-world, spatially-varying surface reflectance from a small number of object views (k). Our key observation is that a specific target's reflectance can be represented by a small number of custom basis materials (N) convexly blended by an even smaller number of non-zero weights at each point (n). Based on this sparse basis/sparser blend model, we develop an SVBRDF reconstruction algorithm that jointly solves for n, N, the basis BRDFs, and their spatial blend weights with an alternating iterative optimization, each step of which solves a linearly-constrained quadratic programming problem. We develop a numerical tool that lets us estimate the number of views required and analyze the effect of lighting and geometry on reconstruction quality. We validate our method with images rendered from synthetic BRDFs, and demonstrate convincing results on real objects of pre-scanned shape and lit by uncontrolled natural illumination, from very few or even a single input image.