Image space feature detection is the act of selecting points or parts of an image that are easy to distinguish from the surrounding image region. By combining a repeatable point detection with a descriptor, parts of an image can be matched with one another, which is useful in applications like estimating pose from camera input or rectifying images. Recently, precise indoor tracking has started to become important for Augmented and Virtual reality as it is necessary to allow positioning of a headset in 3D space without the need for external tracking devices. Several modern feature detectors use homographies to simulate different viewpoints, not only to train feature detection and description, but test them as well. The problem is that, often, views of indoor spaces contain high depth disparity. This makes the approximation that a homography applied to an image represents a viewpoint change inaccurate. We claim that in order to train detectors to work well in indoor environments, they must be robust to this type of geometry, and repeatable under true viewpoint change instead of homographies. Here we focus on the problem of detecting repeatable feature locations under true viewpoint change. To this end, we generate labeled 2D images from a photo-realistic 3D dataset. These images are used for training a neural network based feature detector. We further present an algorithm for automatically generating labels of repeatable 2D features, and present a fast, easy to use test algorithm for evaluating a detector in an 3D environment.
翻译:图像空间特征探测是选择与周围图像区域区别容易的图像点或部分的动作。 通过将重复点探测与描述符结合起来, 图像的部分可以相互匹配, 这对于通过相机输入或纠正图像来估计图像的相貌等应用是有用的。 最近, 精确的室内跟踪开始变得对增强和虚拟现实非常重要, 因为有必要允许在3D空间定位耳机而不需要外部跟踪设备。 几个现代特征探测器使用同质谱模拟不同的观点, 不仅用于训练特征探测和描述, 而且还测试它们。 问题在于室内空间的视图往往含有高度的深度差异。 这使得对图像应用的同质表达表示观点变化不准确的近似性。 我们声称, 为了在室内环境中良好运行探测器, 它们必须坚固到这种类型的地理测量, 并且可以在真实的观点变化下重复出现。 这里我们集中关注在真实视图变化下探测可重复的特征位置的问题。 我们为此从一个光- 3D 图像的标签图像标记上标记了2 D, 一个基于当前光- 反复的图像的测试模型使用。