The combination of a pattern projector and a camera is widely used for 3D measurement. To recover shape from a captured image, various kinds of depth cues are extracted from projected patterns in the image, such as disparities from active stereo or blurriness for depth from defocus. Recently, several techniques have been proposed to improve 3D quality using multiple depth cues by installing coded apertures in projectors or by increasing the number of projectors. However, superposition of projected patterns forms a complicated light field in 3D space, which makes the process of analyzing captured images challenging. In this paper, we propose a learning-based technique to extract depth information from such a light field, which includes multiple depth cues. In the learning phase, prior to the 3D measurement of unknown scenes, projected patterns as they appear at various depths are prepared from not only actual images but also ones generated virtually using computer graphics and geometric calibration results. Then, we use principal component analysis (PCA) to extract features of small patches. In the 3D measurement (reconstruction) phase, the same features of patches are extracted from a captured image of a target scene and compared with the learned data. By using the dimensional reduction by feature extraction, an efficient search algorithm, such as an approximated nearest neighbor (ANN), can be used for the matching process. Another important advantage of our learning-based approach is that we can use most known projection patterns without changing the algorithm.

中文翻译：

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基于视频投影仪创建的光场的基于学习的主动3D测量技术

图案投影仪和照相机的组合被广泛用于3D测量。为了从捕获的图像中恢复形状，需要从图像中的投影图案中提取各种深度提示，例如，主动立体感的差异或散焦导致的深度模糊。近来，已经提出了几种技术，以通过在投影仪中安装编码孔或增加投影仪的数量来使用多个深度提示来提高3D质量。但是，投影图案的叠加会在3D空间中形成一个复杂的光场，这使得分析捕获图像的过程具有挑战性。在本文中，我们提出了一种基于学习的技术来从这种光场中提取深度信息，其中包括多个深度线索。在学习阶段，在对未知场景进行3D测量之前，不仅在实际图像中，而且在使用计算机图形和几何校正结果虚拟生成的图像中，都准备了在各种深度出现的投影图案。然后，我们使用主成分分析（PCA）提取小补丁的特征。在3D测量（重建）阶段，从目标场景的捕获图像中提取补丁的相同特征，并将其与学习数据进行比较。通过使用基于特征提取的降维，可以将有效的搜索算法（例如近似最近邻居（ANN））用于匹配过程。我们基于学习的方法的另一个重要优点是，我们可以使用大多数已知的投影模式而无需更改算法。