论文标题
从单一视图极化图像透明形状
Transparent Shape from a Single View Polarization Image
论文作者
论文摘要
本文提出了一种基于学习的方法，用于从单个视图极化图像中透明的表面估计。由于固有的传输干扰大大降低了基于物理学的先验的可靠性，因此从极化（SFP）方法产生的现有形状在估计透明形状方面很难。为了应对这一挑战，我们提出了基于物理学的先验的概念，该概念的灵感来自极化图像中的传输组件的噪声多于反射。置信度用于确定基于物理学的先验的贡献。然后，我们使用多分支体系结构构建一个网络（TransSFP），以避免破坏不同层次输入之间的关系。为了训练和测试我们的方法，我们构建了一个数据集，以通过成对的极化图像和地面正常地图从极化形状透明形状。广泛的实验和比较证明了我们方法的卓越精度。
This paper presents a learning-based method for transparent surface estimation from a single view polarization image. Existing shape from polarization(SfP) methods have the difficulty in estimating transparent shape since the inherent transmission interference heavily reduces the reliability of physics-based prior. To address this challenge, we propose the concept of physics-based prior, which is inspired by the characteristic that the transmission component in the polarization image has more noise than reflection. The confidence is used to determine the contribution of the interfered physics-based prior. Then, we build a network(TransSfP) with multi-branch architecture to avoid the destruction of relationships between different hierarchical inputs. To train and test our method, we construct a dataset for transparent shape from polarization with paired polarization images and ground-truth normal maps. Extensive experiments and comparisons demonstrate the superior accuracy of our method.
