Un-EVIMO: Unsupervised Event-based Independent Motion Segmentation

Ziyun Wang*, Jinyuan Guo, Kostas Daniilidis ;

"Event cameras are a novel type of biologically inspired vision sensor known for their high temporal resolution, high dynamic range, and low power consumption. Because of these properties, they are well-suited for processing fast motions that require rapid reactions. Event cameras have shown competitive performance in unsupervised optical flow estimation. However, performance in detecting independently moving objects (IMOs) is lacking behind, although event-based methods would be suited for this task based on their low latency and HDR properties. Previous approaches to event-based IMO segmentation heavily depended on labeled data. However, biological vision systems have developed the ability to avoid moving objects through daily tasks without using explicit labels. In this work, we propose the first event framework that generates IMO pseudo-labels using geometric constraints. Due to its unsupervised nature, our method can flexibly handle a non-predetermined arbitrary number of objects and is easily scalable to datasets where expensive IMO labels are not readily available. Our approach shows competitive performance on the EVIMO dataset compared with supervised methods, both quantitatively and qualitatively. See the project website for details: https://www.cis.upenn.edu/~ziyunw/un_evimo/."