3D MORPHABLE MODEL

• Definición

Un modelo facial deformable 3D, (tema importante y actual en la investigación en visión por computadora). 3DMM construye estadísticamente un modelo conjunto basado en la forma 3D y la textura en 2D. 3DMM puede utilizarse para la animación facial 3D y potencialmente útil para el reconocimiento, entre otras.

• Historia

3DMM fue propuesto por primera vez por el prof. V. Blanz y prof. T. Vetter ,Universidad de Basel. 3DMM puede considerarse como el hermano de 3D AAM.

Pasos

1.-Alineación 3D-3D

2.-Establecer Correspondencia

2.1.-Asegurar que cada punto tenga la misma posición en forma y textura para cada modelo

2.2.-Registración basada en flujo óptico

RecommendationsThere are several parts of the system that can be improved these improvements are listedin this section as recommendations for future research and development regarding 3D facerecognitioning image acquisition.(6.2.1) Replace the current 2D face recognitioning method to roughly crop the 3D image to a3D version.(6.2.2) Create a better hole filling method using interpolation between different depth images.(6.2.3) Create a better noise filtering method to reduce the amount of distance deviation in thedepth images. (6.2.4) Speed up the image acquisition process by using a faster processing language.(6.2.5) Replace the facial feature extraction method with a smarter algorithm so the facialfeatures can be extracted without having to face the camera.

GROUND TRUTH

"Ground truth" means a set of measurements that is known to be much more accurate than measurements from the system you are testing.

For example, suppose you are testing a stereo vision system to see how well it can estimate 3D positions. The "ground truth" might be the positions given by a laser rangefinder which is known to be much more accurate than the camera system.

Sometimes synthetic data are generated from a model, to test a system whose goal is to estimate parameters of the model. In such cases the "ground truth" is the known parameters of the model. Again stereo vision provides a good example: computer graphics can generate synthetic images from a 3D model, and then a stereo vision system can try to reconstruct the model. The original model is the "ground truth".

In the case of edge detection, it's much less clear what "ground truth" means, and in fact I don't think it's well defined, as edge detection generally involves making some (more or less) arbitrary choices. Nonetheless, I can think of two possible candidates: one is the output of an edge-detector

which is generally accepted to be high-quality; the other is the edges drawn by an expert human looking at the image. Neither of these is really ground truth, however, and the term "gold standard" would be more applicable.

This comes down to a question to you: what is your ultimate goal? That is, why do you want "ground truth"?