AccFlow: Backward Accumulation for Long-Range Optical Flow
Guangyang Wu Xiaohong Liu Kunming Luo Xi Liu Qingqing Zheng Shuaicheng Liu Xinyang Jiang Guangtao Zhai Wenyi Wang
Recent deep learning-based optical flow estimators have exhibited impressive
performance in generating local flows between consecutive frames. However, the
estimation of long-range flows between distant frames, particularly under
complex object deformation and large motion occlusion, remains a challenging
task. One promising solution is to accumulate local flows explicitly or
implicitly to obtain the desired long-range flow. Nevertheless, the
accumulation errors and flow misalignment can hinder the effectiveness of this
approach. This paper proposes a novel recurrent framework called AccFlow, which
recursively backward accumulates local flows using a deformable module called
as AccPlus. In addition, an adaptive blending module is designed along with
AccPlus to alleviate the occlusion effect by backward accumulation and rectify
the accumulation error. Notably, we demonstrate the superiority of backward
accumulation over conventional forward accumulation, which to the best of our
knowledge has not been explicitly established before. To train and evaluate the
proposed AccFlow, we have constructed a large-scale high-quality dataset named
CVO, which provides ground-truth optical flow labels between adjacent and
distant frames. Extensive experiments validate the effectiveness of AccFlow in
handling long-range optical flow estimation. Codes are available at
https://github.com/mulns/AccFlow .