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.

翻译：受基于帧的方法启发，现有最优事件驱动光流网络依赖计算代价与存储开销高昂的相关性体积显式计算，这在计算预算与内存有限的系统上难以部署。为此，本文提出IDNet（迭代去模糊网络），一种无需相关性体积的轻量级高性能事件驱动光流网络。IDNet利用事件流独特的时空连续特性，通过迭代优化与运动去模糊隐式捕获相关性。该网络不计算相关性体积，而是采用循环网络迭代最大化事件的时空相关性。我们进一步提出两种迭代更新方案：对同一批事件进行迭代的"ID"方案，以及以在线方式对流式事件进行时间迭代的"TID"方案。基准测试表明，前者"ID"方案在节省33%计算量与90%内存占用的前提下接近最优性能；后者"TID"方案更高效，虽牺牲18%性能，但可实现83%计算量节省与15倍延迟降低。