Cargando…
Evaluating the Accuracy of the Azure Kinect and Kinect v2
The Azure Kinect represents the latest generation of Microsoft Kinect depth cameras. Of interest in this article is the depth and spatial accuracy of the Azure Kinect and how it compares to its predecessor, the Kinect v2. In one experiment, the two sensors are used to capture a planar whiteboard at...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002889/ https://www.ncbi.nlm.nih.gov/pubmed/35408082 http://dx.doi.org/10.3390/s22072469 |
_version_ | 1784685999157149696 |
---|---|
author | Kurillo, Gregorij Hemingway, Evan Cheng, Mu-Lin Cheng, Louis |
author_facet | Kurillo, Gregorij Hemingway, Evan Cheng, Mu-Lin Cheng, Louis |
author_sort | Kurillo, Gregorij |
collection | PubMed |
description | The Azure Kinect represents the latest generation of Microsoft Kinect depth cameras. Of interest in this article is the depth and spatial accuracy of the Azure Kinect and how it compares to its predecessor, the Kinect v2. In one experiment, the two sensors are used to capture a planar whiteboard at 15 locations in a grid pattern with laser scanner data serving as ground truth. A set of histograms reveals the temporal-based random depth error inherent in each Kinect. Additionally, a two-dimensional cone of accuracy illustrates the systematic spatial error. At distances greater than 2.5 m, we find the Azure Kinect to have improved accuracy in both spatial and temporal domains as compared to the Kinect v2, while for distances less than 2.5 m, the spatial and temporal accuracies were found to be comparable. In another experiment, we compare the distribution of random depth error between each Kinect sensor by capturing a flat wall across the field of view in horizontal and vertical directions. We find the Azure Kinect to have improved temporal accuracy over the Kinect v2 in the range of 2.5 to 3.5 m for measurements close to the optical axis. The results indicate that the Azure Kinect is a suitable substitute for Kinect v2 in 3D scanning applications. |
format | Online Article Text |
id | pubmed-9002889 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90028892022-04-13 Evaluating the Accuracy of the Azure Kinect and Kinect v2 Kurillo, Gregorij Hemingway, Evan Cheng, Mu-Lin Cheng, Louis Sensors (Basel) Article The Azure Kinect represents the latest generation of Microsoft Kinect depth cameras. Of interest in this article is the depth and spatial accuracy of the Azure Kinect and how it compares to its predecessor, the Kinect v2. In one experiment, the two sensors are used to capture a planar whiteboard at 15 locations in a grid pattern with laser scanner data serving as ground truth. A set of histograms reveals the temporal-based random depth error inherent in each Kinect. Additionally, a two-dimensional cone of accuracy illustrates the systematic spatial error. At distances greater than 2.5 m, we find the Azure Kinect to have improved accuracy in both spatial and temporal domains as compared to the Kinect v2, while for distances less than 2.5 m, the spatial and temporal accuracies were found to be comparable. In another experiment, we compare the distribution of random depth error between each Kinect sensor by capturing a flat wall across the field of view in horizontal and vertical directions. We find the Azure Kinect to have improved temporal accuracy over the Kinect v2 in the range of 2.5 to 3.5 m for measurements close to the optical axis. The results indicate that the Azure Kinect is a suitable substitute for Kinect v2 in 3D scanning applications. MDPI 2022-03-23 /pmc/articles/PMC9002889/ /pubmed/35408082 http://dx.doi.org/10.3390/s22072469 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kurillo, Gregorij Hemingway, Evan Cheng, Mu-Lin Cheng, Louis Evaluating the Accuracy of the Azure Kinect and Kinect v2 |
title | Evaluating the Accuracy of the Azure Kinect and Kinect v2 |
title_full | Evaluating the Accuracy of the Azure Kinect and Kinect v2 |
title_fullStr | Evaluating the Accuracy of the Azure Kinect and Kinect v2 |
title_full_unstemmed | Evaluating the Accuracy of the Azure Kinect and Kinect v2 |
title_short | Evaluating the Accuracy of the Azure Kinect and Kinect v2 |
title_sort | evaluating the accuracy of the azure kinect and kinect v2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002889/ https://www.ncbi.nlm.nih.gov/pubmed/35408082 http://dx.doi.org/10.3390/s22072469 |
work_keys_str_mv | AT kurillogregorij evaluatingtheaccuracyoftheazurekinectandkinectv2 AT hemingwayevan evaluatingtheaccuracyoftheazurekinectandkinectv2 AT chengmulin evaluatingtheaccuracyoftheazurekinectandkinectv2 AT chenglouis evaluatingtheaccuracyoftheazurekinectandkinectv2 |