论文标题
praflow_rvc：金字塔复发的全对场转换，用于稳健视觉挑战中的光流估计2020
PRAFlow_RVC: Pyramid Recurrent All-Pairs Field Transforms for Optical Flow Estimation in Robust Vision Challenge 2020
论文作者
论文摘要
光流估计是一项重要的计算机视觉任务，旨在估计两个帧之间的密集对应关系。筏（反复发作的所有对场变换）当前代表光流估计中的最先进。它具有出色的概括能力，并在几个基准中获得了出色的结果。为了进一步提高鲁棒性并实现准确的光流估计，我们提出了基于金字塔网络结构的PRAFLOF（金字塔复发全对流）。由于计算限制，我们提出的网络结构仅使用两个金字塔层。在每一层，筏单元用于估计电流分辨率下的光流。我们的模型经过了几个模拟和现实图像数据集的培训，使用相同的模型和参数提交给多个排行榜，并在ECCV 2020讲习班的光流任务中赢得了第二名：强大的视觉挑战。
Optical flow estimation is an important computer vision task, which aims at estimating the dense correspondences between two frames. RAFT (Recurrent All Pairs Field Transforms) currently represents the state-of-the-art in optical flow estimation. It has excellent generalization ability and has obtained outstanding results across several benchmarks. To further improve the robustness and achieve accurate optical flow estimation, we present PRAFlow (Pyramid Recurrent All-Pairs Flow), which builds upon the pyramid network structure. Due to computational limitation, our proposed network structure only uses two pyramid layers. At each layer, the RAFT unit is used to estimate the optical flow at the current resolution. Our model was trained on several simulate and real-image datasets, submitted to multiple leaderboards using the same model and parameters, and won the 2nd place in the optical flow task of ECCV 2020 workshop: Robust Vision Challenge.
