Title
A naturalistic open source movie for optical flow evaluation
Abstract
Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.
Year
DOI
Venue
2012
10.1007/978-3-642-33783-3_44
ECCV (6)
Keywords
Field
DocType
new optical flow data,graphics data,optical flow estimation,existing flow algorithm,ground truth optical flow,complex data,optical flow evaluation,naturalistic open source movie,optical flow,optical flow data set,open source
Data set,Computer graphics (images),Computer science,Complex data type,Synthetic data,Artificial intelligence,Graphics,Computer vision,Flow (psychology),Motion blur,Ground truth,Optical flow,Machine learning
Conference
Volume
ISSN
Citations 
7577
0302-9743
264
PageRank 
References 
Authors
7.46
11
4
Search Limit
100264
Name
Order
Citations
PageRank
Daniel J. Butler131310.20
Jonas Wulff243817.59
Garrett B. Stanley328411.10
Michael J. Black4112331536.41