Beschreibung:
<jats:p>Three-dimensional personalized human avatars have been successfully utilized
in shopping, entertainment, education, and health applications. However, it
is still a challenging task to obtain both a complete and highly detailed
avatar automatically. One approach is to use general-purpose,
photogrammetry-based algorithms on a series of overlapping images of the
person. We argue that the quality of avatar reconstruction can be increased
by modifying parts of the photogrammetry-based algorithm pipeline to be more
specifically tailored to the human body shape. In this context, we perform
an extensive, standalone evaluation of eleven algorithms for keypoint
detection, which is the first phase of the photogrammetry-based
reconstruction pipeline. We include well established, patented Distinctive
image features from scale-invariant keypoints (SIFT) and Speeded up robust
features (SURF) detection algorithms as a baseline since they are widely
incorporated into photogrammetry-based software. All experiments are
conducted on a dataset of 378 images of human body captured in a controlled,
multi-view stereo setup. Our findings are that binary detectors highly
outperform commonly used SIFT-like detectors in the avatar reconstruction
task, both in terms of detection speed and in number of detected keypoints.</jats:p>