Selection of Key Frames for 3D Reconstruction in Real Time

Cameras play a prominent role in the context of 3D data, as they can be designed to be very cheap and small and can therefore be used in many 3D reconstruction systems. Typical cameras capture video at 20 to 60 frames per second, resulting in a high number of frames to select from for 3D reconstruct...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alan Koschel, Christoph Müller, Alexander Reiterer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	supervised learning classification key frame selection real time inertial sensing Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/ab1ea1935e9a46388bbaa8e3c6a19c3e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:ab1ea1935e9a46388bbaa8e3c6a19c3e
record_format	dspace
spelling	oai:doaj.org-article:ab1ea1935e9a46388bbaa8e3c6a19c3e2021-11-25T16:12:52ZSelection of Key Frames for 3D Reconstruction in Real Time10.3390/a141103031999-4893https://doaj.org/article/ab1ea1935e9a46388bbaa8e3c6a19c3e2021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/303https://doaj.org/toc/1999-4893Cameras play a prominent role in the context of 3D data, as they can be designed to be very cheap and small and can therefore be used in many 3D reconstruction systems. Typical cameras capture video at 20 to 60 frames per second, resulting in a high number of frames to select from for 3D reconstruction. Many frames are unsuited for reconstruction as they suffer from motion blur or show too little variation compared to other frames. The camera used within this work has built-in inertial sensors. What if one could use the built-in inertial sensors to select a set of key frames well-suited for 3D reconstruction, free from motion blur and redundancy, in real time? A random forest classifier (RF) is trained by inertial data to determine frames without motion blur and to reduce redundancy. Frames are analyzed by the fast Fourier transformation and Lucas–Kanade method to detect motion blur and moving features in frames to label those correctly to train the RF. We achieve a classifier that omits successfully redundant frames and preserves frames with the required quality but exhibits an unsatisfied performance with respect to ideal frames. A 3D reconstruction by Meshroom shows a better result with selected key frames by the classifier. By extracting frames from video, one can comfortably scan objects and scenes without taking single pictures. Our proposed method automatically extracts the best frames in real time without using complex image-processing algorithms.Alan KoschelChristoph MüllerAlexander ReitererMDPI AGarticlesupervised learningclassificationkey frame selectionreal timeinertial sensingIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 303, p 303 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	supervised learning classification key frame selection real time inertial sensing Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	supervised learning classification key frame selection real time inertial sensing Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Alan Koschel Christoph Müller Alexander Reiterer Selection of Key Frames for 3D Reconstruction in Real Time
description	Cameras play a prominent role in the context of 3D data, as they can be designed to be very cheap and small and can therefore be used in many 3D reconstruction systems. Typical cameras capture video at 20 to 60 frames per second, resulting in a high number of frames to select from for 3D reconstruction. Many frames are unsuited for reconstruction as they suffer from motion blur or show too little variation compared to other frames. The camera used within this work has built-in inertial sensors. What if one could use the built-in inertial sensors to select a set of key frames well-suited for 3D reconstruction, free from motion blur and redundancy, in real time? A random forest classifier (RF) is trained by inertial data to determine frames without motion blur and to reduce redundancy. Frames are analyzed by the fast Fourier transformation and Lucas–Kanade method to detect motion blur and moving features in frames to label those correctly to train the RF. We achieve a classifier that omits successfully redundant frames and preserves frames with the required quality but exhibits an unsatisfied performance with respect to ideal frames. A 3D reconstruction by Meshroom shows a better result with selected key frames by the classifier. By extracting frames from video, one can comfortably scan objects and scenes without taking single pictures. Our proposed method automatically extracts the best frames in real time without using complex image-processing algorithms.
format	article
author	Alan Koschel Christoph Müller Alexander Reiterer
author_facet	Alan Koschel Christoph Müller Alexander Reiterer
author_sort	Alan Koschel
title	Selection of Key Frames for 3D Reconstruction in Real Time
title_short	Selection of Key Frames for 3D Reconstruction in Real Time
title_full	Selection of Key Frames for 3D Reconstruction in Real Time
title_fullStr	Selection of Key Frames for 3D Reconstruction in Real Time
title_full_unstemmed	Selection of Key Frames for 3D Reconstruction in Real Time
title_sort	selection of key frames for 3d reconstruction in real time
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/ab1ea1935e9a46388bbaa8e3c6a19c3e
work_keys_str_mv	AT alankoschel selectionofkeyframesfor3dreconstructioninrealtime AT christophmuller selectionofkeyframesfor3dreconstructioninrealtime AT alexanderreiterer selectionofkeyframesfor3dreconstructioninrealtime
_version_	1718413275825176576

Selection of Key Frames for 3D Reconstruction in Real Time

Ejemplares similares