论文标题
波尔卡线：学习主动立体声的结构化照明和重建
Polka Lines: Learning Structured Illumination and Reconstruction for Active Stereo
论文作者
论文摘要
从结构化的光捕获中恢复深度的主动立体声摄像机已成为3D场景重建的基石传感器模式，并了解跨应用程序域的任务。现有的主动立体声摄像机在对象表面上项目一个伪随机点模式，以独立于对象纹理提取差异。这种手工制作的模式是与场景统计，环境照明条件和重建方法隔离设计的。在这项工作中，我们提出了第一种共同学习结构化照明和重建的方法，该方法是通过衍射光学元素和神经网络以端到端方式进行的。为此，我们介绍了一个新型的可区分图像形成模型，用于主动立体声，依靠波和几何光学元件以及一个新型的三眼重建网络。我们将“波尔卡线”的共同优化模式与重建网络相结合，在成像条件上实现了最新的活动性深度估计。我们在模拟和硬件原型中验证了所提出的方法，并表明我们的方法表现优于现有的活动立体声系统。
Active stereo cameras that recover depth from structured light captures have become a cornerstone sensor modality for 3D scene reconstruction and understanding tasks across application domains. Existing active stereo cameras project a pseudo-random dot pattern on object surfaces to extract disparity independently of object texture. Such hand-crafted patterns are designed in isolation from the scene statistics, ambient illumination conditions, and the reconstruction method. In this work, we propose the first method to jointly learn structured illumination and reconstruction, parameterized by a diffractive optical element and a neural network, in an end-to-end fashion. To this end, we introduce a novel differentiable image formation model for active stereo, relying on both wave and geometric optics, and a novel trinocular reconstruction network. The jointly optimized pattern, which we dub "Polka Lines," together with the reconstruction network, achieve state-of-the-art active-stereo depth estimates across imaging conditions. We validate the proposed method in simulation and on a hardware prototype, and show that our method outperforms existing active stereo systems.
