Research Topic: 3D & Event

Event cameras asynchronously measure brightness changes in a scene without motion blur or saturation, while frame cameras capture images with dense intensity and fine details at a fixed rate. The exclusive advantages of the two modalities make depth estimation from Stereo Asymmetric Frame-Event (SAFE) systems appealing. However, due to the inevitable information absence of one modality in certain challenging regions, existing stereo matching methods lose efficacy for asymmetric inputs from SAFE systems. In this paper, we propose a divide-and-conquer approach that decomposes depth estimation from SAFE systems into three sub-tasks, i.e., frame-event stereo matching, frame-based Structure-from-Motion (SfM), and event-based SfM. In this way, the above challenging regions are addressed by monocular SfM, which estimates robust depth with two views belonging to the same functioning modality. Moreover, we propose a dual sampling strategy to construct cost volumes with identical spatial locations and depth hypotheses for different sub-tasks, which enables sub-task fusion at the cost volume level. To tackle the occlusion issue raised by the sampling strategy, we further introduce a temporal fusion scheme to utilize long-term sequential inputs with multi-view information. Experimental results validate the superior performance of our method over existing solutions.