Event-based Stereo Depth Estimation By Temporal-spatial Context Learning

Event cameras represent a cutting-edge sensor technology, recording asynchronous pixel-level intensity changes with high temporal resolution and a wide dynamic range. These attributes make event-based stereo depth estimation particularly robust for scenarios characterized by rapid changes and challenging lighting conditions. However, previous learning-based approaches for event-based stereo have often overlooked exploiting the temporal context information within the scene, resulting in suboptimal depth estimations. In this paper, we introduce a novel learning-based network for event-based stereo that incorporates two innovative modules: the Event-based Temporal Aggregation Module (E-TAM) and the Temporal-guided Spatial Context Learning Module (T-SCLM). The E-TAM is designed to capture temporal context information among temporal features extracted from the entire event stream, further the T-SCLM exploits the temporal context information to provide guidance for spatial context learning. Subsequently, these merged features are input into the stereo matching network, ultimately yielding the final disparity map. Experimental evaluations conducted on two real-world datasets affirm the superiority of our method when compared to state-of-the-art approaches.