论文标题
材料分割的角度亮度
Angular Luminance for Material Segmentation
论文作者
论文摘要
移动摄像机每个像素提供多个强度测量，但通常语义分割，材料识别和对象识别不会利用此信息。在几个移动摄像机序列的几个帧上进行基本对齐，可以获得多个角度的强度分布。从先前的工作中众所周知，亮度直方图和自然图像的统计数据提供了强大的材料识别提示。我们将每个像素{\ IT角亮度分布}用作区分表面材料的关键特征。多视卫星图像序列中的角空间采样是对材料的基本反射函数的非结构化采样。对于现实世界的材料，可以通过构建角度亮度网络（Anglnet）来管理阶层内的显着变化。该网络结合了来自带有空间提示的多个图像的角度反射率提示，作为用于材料分割的完全卷积网络的输入。我们证明了Anglnet对从卫星图像的物质分割中先前最新的表现提高的性能。
Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of a moving camera sequence, a distribution of intensities over multiple angles is obtained. It is well known from prior work that luminance histograms and the statistics of natural images provide a strong material recognition cue. We utilize per-pixel {\it angular luminance distributions} as a key feature in discriminating the material of the surface. The angle-space sampling in a multiview satellite image sequence is an unstructured sampling of the underlying reflectance function of the material. For real-world materials there is significant intra-class variation that can be managed by building a angular luminance network (AngLNet). This network combines angular reflectance cues from multiple images with spatial cues as input to fully convolutional networks for material segmentation. We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.
