Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
75
0
1

Year Published

2011
2011
2019
2019

Publication Types

Select...
5
3
1

Relationship

1
8

Authors

Journals

citations
Cited by 79 publications
(76 citation statements)
references
References 15 publications
0
75
0
1
Order By: Relevance
“…Successful navigation based on a dense disparity map with limited frame-rate is shown. In [11], the authors present a method to estimate the three dimensional motion field out of stereo sequences. A real-time implementation with a combination of FPGA and GPU is able to calculate a dense motion field at 10 frames per second.…”
Section: @Ijrter-2017 All Rights Reserved 367mentioning
confidence: 99%
“…Successful navigation based on a dense disparity map with limited frame-rate is shown. In [11], the authors present a method to estimate the three dimensional motion field out of stereo sequences. A real-time implementation with a combination of FPGA and GPU is able to calculate a dense motion field at 10 frames per second.…”
Section: @Ijrter-2017 All Rights Reserved 367mentioning
confidence: 99%
“…Yang et al [42] detect pixels on moving objects based on optical flow and appearance information and then reconstruct static and dynamic elements of the scene by modeling it as a collection of rigid bodies. Also relevant to our work are variational methods that enforce temporal consistency either by making predictions about the depth or disparity of the next frame [12,24], by using a Kalman filter per point [30] or by approximating the scene as a collection of rigidly moving planar patches [40].…”
Section: Related Workmentioning
confidence: 99%
“…Conversely, [18,13] present a framework for computation of scene flow that separates stereo matching from scene flow computation. They argue that it is an advantage since the user is free to choose stereo and optical flow algorithm with best properties.…”
Section: Stereo and Scene Flowmentioning
confidence: 99%
“…In a similar fashion [3] describe a stereo algorithm with joint estimation of scene flow based on seed growing. The scene flow is grown from stereo matches from previous frame, then the scene flow is used to predict matching in the next frame.Conversely, [18,13] present a framework for computation of scene flow that separates stereo matching from scene flow computation. They argue that it is an advantage since the user is free to choose stereo and optical flow algorithm with best properties.…”
mentioning
confidence: 99%