论文标题
NOCAL：无校准的半监督学习和相机内在学习
NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics
论文作者
论文摘要
有许多新兴成像技术可以使机器人技术受益。但是，对定制模型，校准和低级处理的需求代表了其采用的关键障碍。在这项工作中，我们使用光场介绍了NOCAL，神经渗透和校准，这是一种半监督的学习体系结构，能够解释以前看不见的相机而无需校准。 Nocal学会了估计相机参数，相对姿势和场景外观。它采用了在许多现有的摄像机和场景上仔细预测的场景范围，并使用小型监督训练套件适应以前看不见的摄像机，以执行公制量表。我们使用常规摄像机展示了渲染和捕获的图像的NOCAL，展示了无校准的探光仪和新型视图合成。这项工作代表了自动化一般摄像机几何形状和新兴成像技术的解释的关键步骤。
There are a multitude of emerging imaging technologies that could benefit robotics. However the need for bespoke models, calibration and low-level processing represents a key barrier to their adoption. In this work we present NOCaL, Neural odometry and Calibration using Light fields, a semi-supervised learning architecture capable of interpreting previously unseen cameras without calibration. NOCaL learns to estimate camera parameters, relative pose, and scene appearance. It employs a scene-rendering hypernetwork pretrained on a large number of existing cameras and scenes, and adapts to previously unseen cameras using a small supervised training set to enforce metric scale. We demonstrate NOCaL on rendered and captured imagery using conventional cameras, demonstrating calibration-free odometry and novel view synthesis. This work represents a key step toward automating the interpretation of general camera geometries and emerging imaging technologies.
