Stereo Depth Estimation Based on Adaptive Stacks from Event Cameras

Abstract

In recent years, the combination of event cameras and computer vision has shown increasingly excellent performance. Due to high sensitivity, event cameras are capable of addressing the issue of motion blur in conventional cameras, and are well-suited for analyzing fast-moving objects, making them highly suitable for depth estimation in UAV applications This paper focuses on methods for depth estimation using events generated by event cameras. Due to the asynchronicity of events, it is difficult to directly transmit events to the depth estimation network. So the method to preprocess events is important. Unlike existing processing methods, this paper creatively proposes the idea of adaptive stacks, which can change the size of weighted stacks in real time according to the events generation rate. In this way, we can solve the problems caused by traditional processing methods, and better utilize the effective information of events. Then, a depth estimation network corresponding to the adaptive stacks is designed to form a complete end-to-end events depth estimation model: Adaptive Stacks Depth Estimation Network (ASNet). Compared with other models, ASNet has demonstrated excellent depth estimation accuracy and has great application prospects.