Practical Measurements of Translucent Materials with Inter-Pixel Translucency Prior

Zhenyu Chen, Jie Guo, Shuichang Lai, Ruoyu Fu, Mengxun Kong, Chen Wang, Hongyu Sun, Zhebin Zhang, Chen Li, Yanwen Guo; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 20932-20942

Material appearance is a key component of photorealism with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g. consumer-level cameras) little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper we present a practical method for acquiring scattering properties of translucent materials based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders a translucency-aware feature fusion module and a cascaded decoder. We demonstrate through both visual comparisons and quantitative evaluations that high accuracy can be achieved on a wide range of real-world translucent materials.