Computer vision for autonomous vehicles: Problems, datasets and state of the art
Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …
Traditional and modern strategies for optical flow: an investigation
STH Shah, X Xuezhi - SN Applied Sciences, 2021 - Springer
Abstract Optical Flow Estimation is an essential component for many image processing
techniques. This field of research in computer vision has seen an amazing development in …
techniques. This field of research in computer vision has seen an amazing development in …
Gmflow: Learning optical flow via global matching
Learning-based optical flow estimation has been dominated with the pipeline of cost volume
with convolutions for flow regression, which is inherently limited to local correlations and …
with convolutions for flow regression, which is inherently limited to local correlations and …
Raft: Recurrent all-pairs field transforms for optical flow
Abstract We introduce Recurrent All-Pairs Field Transforms (RAFT), a new deep network
architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D …
architecture for optical flow. RAFT extracts per-pixel features, builds multi-scale 4D …
Liteflownet: A lightweight convolutional neural network for optical flow estimation
Abstract 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 …
estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper …
Pwc-net: Cnns for optical flow using pyramid, war**, and cost volume
We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net
has been designed according to simple and well-established principles: pyramidal …
has been designed according to simple and well-established principles: pyramidal …
Df-net: Unsupervised joint learning of depth and flow using cross-task consistency
We present an unsupervised learning framework for simultaneously training single-view
depth prediction and optical flow estimation models using unlabeled video sequences …
depth prediction and optical flow estimation models using unlabeled video sequences …
Flownet 2.0: Evolution of optical flow estimation with deep networks
The FlowNet demonstrated that optical flow estimation can be cast as a learning problem.
However, the state of the art with regard to the quality of the flow has still been defined by …
However, the state of the art with regard to the quality of the flow has still been defined by …
Global matching with overlap** attention for optical flow estimation
Optical flow estimation is a fundamental task in computer vision. Recent direct-regression
methods using deep neural networks achieve remarkable performance improvement …
methods using deep neural networks achieve remarkable performance improvement …
Video frame interpolation via adaptive separable convolution
Standard video frame interpolation methods first estimate optical flow between input frames
and then synthesize an intermediate frame guided by motion. Recent approaches merge …
and then synthesize an intermediate frame guided by motion. Recent approaches merge …