FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
Significant progress has been made for estimating optical flow using deep neural networks. Advanced deep models achieve accurate flow estimation often with a considerable computation complexity and time-consuming training processes. In this work, we present a lightweight yet effective model for real-time optical flow estimation, termed FDFlowNet (fast deep flownet). We achieve better or similar accuracy on the challenging KITTI and Sintel benchmarks while being about 2 times faster than PWC-Net. This is achieved by a carefully-designed structure and newly proposed components. We first introduce an U-shape network for constructing multi-scale feature which benefits upper levels with global receptive field compared with pyramid network. In each scale, a partial fully connected structure with dilated convolution is proposed for flow estimation that obtains a good balance among speed, accuracy and number of parameters compared with sequential connected and dense connected structures. Experiments demonstrate that our model achieves state-of-the-art performance while being fast and lightweight.
Code (0)
등록된 구현이 없습니다.
Tasks
Optical Flow EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation
Dense optical flow estimation plays a key role in many robotic vision tasks. In the past few years, with the advent of deep learning, we have witnessed great progress in optical flow estimation. However, current networks…
DecoderGPUOptical Flow EstimationCross domain knowledge compression in realtime optical flow prediction on ultrasound sequences
In this paper, we propose an approach for realtime optical flow estimation in ultrasound sequences of vein and arteries based on knowledge distillation. Knowledge distillation is a technique to train a faster, smaller mo…
Knowledge DistillationOptical Flow EstimationA Lightweight Optical Flow CNN -- Revisiting Data Fidelity and Regularization
Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutiona…
Optical Flow EstimationHMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
In optical flow estimation task, coarse-to-fine (C2F) warping strategy is widely used to deal with the large displacement problem and provides efficiency and speed. However, limited by the small search range between the …
Optical Flow EstimationLiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper we present an alternative network that outpe…
Optical Flow Estimation