PolarMatte: Fully Computational Ground-Truth-Quality Alpha Matte Extraction for Images and Video using Polarized Screen Matting

Kenji Enomoto, TJ Rhodes, Brian Price, Gavin Miller; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 3901-3909

The creation of high-quality alpha mattes as ground-truth data for video matting is typically a laborious task. The trade-off between accuracy manual corrections and capture constraints often produces erroneous results or is cost prohibitive. We propose PolarMatte a fully computational alpha matte extraction method for images and video without compromise between quality and practicality. A single polarization camera is used to capture dynamic scenes backlit by an off-the-shelf LCD monitor. PolarMatte exploits the polarization channel to compute the per-pixel opacity of the target scene including the transparency of fine-details translucent objects and optical/motion blur. We leverage polarization clues to robustly detect indistinguishable pixels and extract the alpha matte value at polarized foreground reflections with a polarimetric matting Laplacian. Quantitative and qualitative evaluation demonstrate our ability to computationally extract ground-truth-quality alpha mattes without human labour.