Unsupervised Learning of Dense Optical Flow, Depth and Egomotion with Event-Based Sensors

Abstract

We present an unsupervised learning pipeline for dense depth, optical flow and egomotion estimation for autonomous driving applications, using the event-based output of the Dynamic Vision Sensor (DVS) as input. The backbone of our pipeline is a bioinspired encoder-decoder neural network architecture - ECN. To train the pipeline, we introduce a covariance normalization technique which resembles the lateral inhibition mechanism found in animal neural systems.Our work is the first monocular pipeline that generates dense depth and optical flow from sparse event data only, and is able to transfer from day to night scenes without any additional training. The network works in self-supervised mode and has just 150k parameters. We evaluate our pipeline on the MVSEC self driving dataset and present results for depth, optical flow and and egomotion estimation. Thanks to the efficient design, we are able to achieve inference rates of 300 FPS on a single Nvidia 1080Ti GPU. Our experiments demonstrate significant improvements upon works that used deep learning on event data, as well as the ability to perform well during both day and night.