info:eu-repo/semantics/article
body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices
Fecha
2020-09Registro en:
Trujillo Jiménez, Magda Alexandra; Navarro, Pablo Eugenio; Pazos, Bruno Alfredo; Morales, Arturo Leonardo; Ramallo, Virginia; et al.; body2vec: 3D Point Cloud Reconstruction for Precise Anthropometry with Handheld Devices; MDPI; Journal of Imaging; 6; 9; 9-2020; 1-14
2313-433X
CONICET Digital
CONICET
Autor
Trujillo Jiménez, Magda Alexandra
Navarro, Pablo Eugenio
Pazos, Bruno Alfredo
Morales, Arturo Leonardo
Ramallo, Virginia
Paschetta, Carolina Andrea
de Azevedo, Soledad
Ruderman, Anahí
Perez, Luis Orlando
Delrieux, Claudio Augusto
Gonzalez-Jose, Rolando
Resumen
Current point cloud extraction methods based on photogrammetry generate large amounts of spurious detections that hamper useful 3D mesh reconstructions or, even worse, the possibility of adequate measurements. Moreover, noise removal methods for point clouds are complex, slow and incapable to cope with semantic noise. In this work, we present body2vec, a model-based body segmentation tool that uses a specifically trained Neural Network architecture. Body2vec is capable to perform human body point cloud reconstruction from videos taken on hand-held devices (smartphones or tablets), achieving high quality anthropometric measurements. The main contribution of the proposed workflow is to perform a background removal step, thus avoiding the spurious points generation that is usual in photogrammetric reconstruction. A group of 60 persons were taped with a smartphone, and the corresponding point clouds were obtained automatically with standard photogrammetric methods. We used as a 3D silver standard the clean meshes obtained at the same time with LiDAR sensors post-processed and noise-filtered by expert anthropological biologists. Finally, we used as gold standard anthropometric measurements of the waist and hip of the same people, taken by expert anthropometrists. Applying our method to the raw videos significantly enhanced the quality of the results of the point cloud as compared with the LiDAR-based mesh, and of the anthropometric measurements as compared with the actual hip and waist perimeter measured by the anthropometrists. In both contexts, the resulting quality of body2vec is equivalent to the LiDAR reconstruction.