Lightweight Event-based Optical Flow Estimation via Iterative Deblurring

Inspired by frame-based methods, state-of-the-art event-based optical flow networks rely on the explicit computation of correlation volumes, which are expensive to compute and store on systems with limited processing budget and memory. To this end, we introduce IDNet (Iterative Deblurring Network), a lightweight yet well-performing event-based optical flow network without using correlation volumes. IDNet leverages the unique spatiotemporally continuous nature of event streams to propose an alternative way of implicitly capturing correlation through iterative refinement and motion deblurring. Our network does not compute correlation volumes but rather utilizes a recurrent network to maximize the spatiotemporal correlation of events iteratively. We further propose two iterative update schemes: "ID" which iterates over the same batch of events, and "TID" which iterates over time with streaming events in an online fashion. Benchmark results show the former "ID" scheme can reach close to state-of-the-art performance with 33% of savings in compute and 90% in memory footprint, while the latter "TID" scheme is even more efficient promising 83% of compute savings and 15 times less latency at the cost of 18% of performance drop.