Recently, optical flow estimation benefits greatly from deep learning based techniques. Most
approaches use encoder-decoder architecture (U-Net) or spatial pyramid network (SPN) to …

Though the accuracy and robustness of optical flow has been dramatically enhanced over
the past few years, the issue of edge-blurring near the image and motion boundaries has …

We introduce a novel training strategy for stereo matching and optical flow estimation that
utilizes image-to-image translation between synthetic and real image domains. Our …

Learning optical flow based on convolutional neural networks has made great progress in
recent years. These approaches usually design an encoder-decoder network that can be …

Traditional methods for estimating optical flow use variational model that includes data term
and smoothness term, which can build a constraint relationship between two adjacent …

Nowadays, convolutional neural networks achieve remarkable performance on optical flow
estimation because of its strong non-linear fitting ability. Most of them adopt the U-Net …

In this paper we propose a novel approach to estimate dense optical flow from sparse lidar
data acquired on an autonomous vehicle. This is intended to be used as a drop-in …

Leveraging on the recent developments in convolutional neural networks (CNNs), optical
flow estimation from adjacent frames has been cast as a learning problem, with performance …

为提升基于编解码架构的U 型网络在深度学习光流估计中的精度, 提出了一种结合注意力机制的
改进有监督深度学习光流网络. 网络由收缩和扩张两部分组成, 收缩部分利用一系列卷积层来 …

Abstract Analysis of cardiac hemodynamics from Doppler echocardiography videos allows
(i) precise diagnosis of circulatory disorders,(ii) assessment of cardiovascular function and …