Model-based real-time non-rigid tracking
Autores
Bronte Palacios, Sebastián; Bergasa Pascual, Luis Miguel; Pizarro Pérez, Daniel; Barea Navarro, RafaelIdentificadores
Enlace permanente (URI): http://hdl.handle.net/10017/43130DOI: 10.3390/s17102342
ISSN: 1424-8220
Editor
MDPI
Fecha de publicación
2017-09-19Patrocinadores
Ministerio de Economía y Competitividad
Comunidad de Madrid
Agencia Estatal de Investigación
Cita bibliográfica
Bronte, S., Bergasa, L. M., Pizarro, D., & Barea, R. 2017, "Model-Based Real-Time Non-Rigid Tracking". Sensors (Basel, Switzerland), 17(10), 2342. doi: 10.3390/s17102342
Palabras clave
NRSfM
SfM
Non-rigid reconstruction
PTAM
Tracking
SfT
Proyectos
info:eu-repo/grantAgreement/MINECO//TRA2015-70501-C2-1-R/ES/VEHICULO INTELIGENTE PARA PERSONAS MAYORES/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-80939-R/ES/RECONSTRUCCION DE OBJETOS DEFORMABLES A PARTIR DE IMAGENES Y SUS APLICACIONES A LA REALIDAD AUMENTADA EN CIRUGIA MINIMAMENTE INVASIVA/
info:eu-repo/grantAgreement/CAM//S2013%2FMIT-2748/ES/ROBOTICA APLICADA A LA MEJORA DE LA CALIDAD DE VIDA DE LOS CIUDADANOS, FASE III/RoboCity2030-III-CM
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.3390/s17102342Derechos
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
This paper presents a sequential non-rigid reconstruction method that recovers the 3D shape and the camera pose of a deforming object from a video sequence and a previous shape model of the object. We take PTAM (Parallel Mapping and Tracking), a state-of-the-art sequential real-time SfM (Structure-from-Motion) engine, and we upgrade it to solve non-rigid reconstruction. Our method provides a good trade-off between processing time and reconstruction error without the need for specific processing hardware, such as GPUs. We improve the original PTAM matching by using descriptor-based features, as well as smoothness priors to better constrain the 3D error. This paper works with perspective projection and deals with outliers and missing data. We evaluate the tracking algorithm performance through different tests over several datasets of non-rigid deforming objects. Our method achieves state-of-the-art accuracy and can be used as a real-time method suitable for being embedded in portable devices.
Ficheros en el ítem
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Colecciones
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