用图像预测BRDF采样点，减少材料测量时间。
Deep image-based Adaptive BRDF Measure
- 输入实物图像，用轻量网络估计BRDF参数和采样分布。
- 基于图像损失计算最少采样数，保证精度与保真度。
- 适合需要快速高精度材质建模的渲染与仿真研究者。
高效准确的双向反射分布函数（BRDF）测量在高质量图像渲染和物理传感器模拟中至关重要。然而，获取材料的反射特性既耗时又具有挑战性。本文提出一种新方法，通过球面反射仪设置最小化高质量BRDF捕获所需的样本数量。以实物材料样本的图像为输入，轻量级神经网络首先估计解析BRDF模型的参数及采样位置分布；第二步利用基于图像的损失函数确定满足精度要求的最少样本数。该方法显著加速了测量过程，同时保持了高精度与高保真度的BRDF表示。
原文摘要 · Abstract (English)
Efficient and accurate measurement of the bi-directional reflectance distribution function (BRDF) plays a key role in high quality image rendering and physically accurate sensor simulation. However, obtaining the reflectance properties of a material is both time-consuming and challenging. This paper presents a novel method for minimizing the number of samples required for high quality BRDF capture using a gonio-reflectometer setup. Taking an image of the physical material sample as input a lightweight neural network first estimates the parameters of an analytic BRDF model, and the distribution of the sample locations. In a second step we use an image based loss to find the number of samples required to meet the accuracy required. This approach significantly accelerates the measurement process while maintaining a high level of accuracy and fidelity in the BRDF representation.
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