AutoRemover: Automatic Object Removal for Autonomous Driving Videos
This addresses the need for high-quality simulation in autonomous driving, though it is an incremental improvement over existing object removal techniques.
The paper tackles the problem of removing moving objects from autonomous driving videos to create photo-realistic simulations, achieving a 19% reduction in RMSE compared to state-of-the-art methods.
Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More specifically, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over $19\%$.