EOMVS: Event-Based Omnidirectional Multi-View Stereo

Abstract

Event-based cameras sense changes in light intensity, and asynchronously outputs event data. Compared with conventional cameras, event-based cameras have the advantages of low latency, high dynamic range, and low power consumption. Event cameras have been utilized for various vision tasks, such as depth estimation and object detection under severe illumination changes and dynamic motion. Fisheye or omnidirectional cameras, on the other hand, have much wider field-of-view (FoV) allowing a more compact system for omnidirectional vision in comparison with multiple normal-FoV camera setups. In this work, we propose a new multi-view stereo method, called EOMVS, to reconstruct a 3D scene with a wide view using the event data captured by omnidirectional event cameras. The proposed method has the advantages of both event and fisheye cameras. To validate our EOMVS method, we conducted experiments using both synthetic and real-world datasets, and evaluated the performance both qualitatively and quantitatively. The experiments show that the presented approach can accurately obtain wide-view 3D information.