期刊名称：Facta universitatis - series: Electronics and Energetics
印刷版ISSN：0353-3670
电子版ISSN：2217-5997
出版年度：2020
卷号：33
期号：3
页码：379-394
DOI：10.2298/FUEE2003379G
出版社：University of Niš
摘要：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.
关键词：Detector; Photogrammetry-based reconstruction; 3D human avatar; Structure from Motion; Multi-view Stereo
