论文标题
对抗纹理优化的初始化和对齐
Initialization and Alignment for Adversarial Texture Optimization
论文作者
论文摘要
虽然从图像和视频数据中恢复几何形状在计算机视觉中引起了很多关注，但捕获给定几何形状纹理的方法不那么成熟。具体而言，纹理生成的经典方法通常假设干净的几何形状和合理的一致图像数据。尽管最近的方法，例如，对抗性纹理优化，更好地处理从手持设备获得的低质量数据，但我们发现它们仍然经常挣扎。为了提高鲁棒性，特别是最近的对抗性纹理优化，我们开发了明确的初始化和一个对齐程序。由于将几何形状绘制到纹理图和基于硬分配的初始化，因此它处理了复杂的几何形状。它通过将快速的图像对齐整合到纹理改进优化中来处理几何和图像的错位。我们在11个场景的数据集中证明了纹理生成的功效，总共有2807帧，观察7.8％和11.1％的感知和清晰度测量相对相对改善。
While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature. Specifically, classical methods for texture generation often assume clean geometry and reasonably well-aligned image data. While very recent methods, e.g., adversarial texture optimization, better handle lower-quality data obtained from hand-held devices, we find them to still struggle frequently. To improve robustness, particularly of recent adversarial texture optimization, we develop an explicit initialization and an alignment procedure. It deals with complex geometry due to a robust mapping of the geometry to the texture map and a hard-assignment-based initialization. It deals with misalignment of geometry and images by integrating fast image-alignment into the texture refinement optimization. We demonstrate efficacy of our texture generation on a dataset of 11 scenes with a total of 2807 frames, observing 7.8% and 11.1% relative improvements regarding perceptual and sharpness measurements.
