3 accepted papers
Most existing event-based camera pose relocalization (CPR) learning methods implicitly encode environmental information into network parameters to achieve end-to-end mapping from event stream to pose. However, these end-to-end CPR methods fail to utilize prior environmental information effectively.
Event cameras have the potential to capture continuous motion information over time and space, making them well-suited for optical flow estimation. However, most existing learning-based methods for event-based optical flow adopt frame-based techniques, ignoring the spatio-temporal characteristics of
Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event ca