Optical flow is the velocity field which warps one image into another (usually very similar) image. This is illustrated by the images below. The sphere is rotating from left to right, generating the optical flow field shown in the center.
 

Image #1		Optical Flow Field		Image #2

Almost 20 years ago Horn & Schunck [1] published the seminal paper on optical flow calculation.  This paper sparked a substantial research effort, producing an abundance of techniques for calculating optical flow. Unfortunately because of the difficulties involved in generating ground truth results for real image sequences, it has been almost impossible to evaluate our progress. Are the new algorithms really any better than Horn & Schunk's technique, published almost 20 years ago?

While there have been previous attempts at evaluating the performance of optical flow techniques, they have usually used simple sequences generated by applying a distortion to a base image, since the corresponding motion field is trivial to compute. Unfortunately, it is difficult to know how relevant these results are to real  3D imagery, with all its associated complexities (for example motion discontinuities, complex 3D surfaces, camera noise, specular highlights, shadows, atmospherics, transparency).

In our evaluation we have used two methods to generate more complex sequences with ground-truth data: a ray-tracer which generates optical flow, and a Tcl/Tk tool which allows us to generate ground truth optical flow from simple (ie polygonal) real sequences with a little help from the user.  The Tcl/Tk tool is included in the released package as are all the sequences. The following images show example frames from each of the sequences.
 
 
 

Rotating Sphere	Office - Cam Zooms in	Street - Cam Follows Car
Rotating Blocks	Translating Grid	C++ Box - Cam Zooms out
Low Complexity	Medium Complexity	High Complexity