论文标题
通过学习的光流和翘曲预测未来实例细分
Forecasting Future Instance Segmentation with Learned Optical Flow and Warping
论文作者
论文摘要
对于自动驾驶汽车，必须观察场景的持续动态，因此必须预测即将来临的情况，以确保自己和他人的安全。这可以使用不同的传感器和方式完成。在本文中，我们研究了光流的用法用于预测未来的语义分割。为此，我们提出了一个模型，该模型可预测流场自动加压。然后，使用此类预测来指导将实例分割转移到未来框架的学习翘曲功能的推断。 CityScapes数据集的结果证明了光流方法的有效性。
For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others. This can be done using different sensors and modalities. In this paper we investigate the usage of optical flow for predicting future semantic segmentations. To do so we propose a model that forecasts flow fields autoregressively. Such predictions are then used to guide the inference of a learned warping function that moves instance segmentations on to future frames. Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow methods.
