Time-to-contact (TTC), the time for an object to collide with the observer's plane, is a powerful tool for path planning: it is potentially more informative than the depth, velocity, and acceleration of objects in the scene -- even for humans. TTC presents several advantages, including requiring only a monocular, uncalibrated camera. However, regressing TTC for each pixel is not straightforward, and most existing methods make over-simplifying assumptions about the scene. We address this challenge by estimating TTC via a series of simpler, binary classifications. We predict with low latency whether the observer will collide with an obstacle within a certain time, which is often more critical than knowing exact, per-pixel TTC. For such scenarios, our method offers a temporal geofence in 6.4 ms -- over 25x faster than existing methods. Our approach can also estimate per-pixel TTC with arbitrarily fine quantization (including continuous values), when the computational budget allows for it. To the best of our knowledge, our method is the first to offer TTC information (binary or coarsely quantized) at sufficiently high frame-rates for practical use.
翻译:时间到接触(TTC) 是一个与观察者平面相撞的物体的时间点,是进行路径规划的有力工具:它可能比现场物体的深度、速度和加速速度 -- -- 甚至对人类而言 -- -- 更具有更大的信息性。 TTC 具有若干优点,包括只需要一个单眼、未经校准的相机。 然而,对每个像素的反向 TTC 并不是简单易行的,而且大多数现有方法对场景的假设过于简单化。我们通过一系列简单、二元分级来估计 TTC 来应对这一挑战。 我们预测观察者是否会在一定时间里遇到障碍,这往往比了解准确、每平方TC TC 的准确性更为关键。 对于这种情形,我们的方法提供了6.4 ms -- -- 超过现有方法的25x。 我们的方法还可以在计算预算允许的情况下,以任意精细的四分化(包括连续值)来估计每平方TTC 。 我们最了解的是,我们的方法是首先为TRC 提供足够高的实用性框架信息。