Time-of-flight sensor and color camera calibration for multi-view acquisition

Shim, Hyunjung; Adelsberger, Rolf; Kim, James Dokyoon; Rhee, Seon-Min; Rhee, Taehyun; Sim, Jae-Young; Groß, Markus; Kim, Chang-Yeong

doi:10.1007/s00371-011-0664-x

Cited by 26 publications

(22 citation statements)

References 4 publications

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“…One of the main cause of the noise in a depth image is a scene depth as analyzed in [13]. To simulate a depth image captured by a depth sensor, we firstly generate gaussian random value with Please note that our method shows more accurate results than RANSAC especially in case of the three-plane case.…”

Section: Synthetic Scenesmentioning

confidence: 99%

Split and merge approach for detecting multiple planes in a depth image

Rhee

Lee

Kim

et al. 2012

2012 19th IEEE International Conference on Image Processing

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We propose a novel method for detecting multiple planar structures in a scene from a depth image, and estimating their parametric models in real time. To realize this goal we split an entire depth image into small patches. Initially, we assume that all patches are on the planar structure and estimate their parametric models using least square fitting. We select patches where our assumption is valid and the selected planar patches are iteratively merged and refined in case that they are in the same planar structure. The qualitative and quantitative experiments shows that the proposed method has clear benefits in terms of accuracy and processing time.

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Section: Synthetic Scenesmentioning

confidence: 99%

Split and merge approach for detecting multiple planes in a depth image

Rhee

Lee

Kim

et al. 2012

2012 19th IEEE International Conference on Image Processing

Self Cite

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“…The ToF camera provided depth information, and the RGB camera was used to obtain color information. Shim et al [14] presented a method to calibrate a multiple view acquisition system composed of ToF cameras and RGB color cameras. This system also has the ability to calibrate multi-modal sensors in real time.…”

Section: Poor Resolutionmentioning

confidence: 99%

Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping

et al. 2017

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Accurate high-resolution three-dimensional (3D) models are essential for a non-invasive analysis of phenotypic characteristics of plants. Previous limitations in 3D computer vision algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present an image-based 3D plant reconstruction system that can be achieved by using a single camera and a rotation stand. Our method is based on the structure from motion method, with a SIFT image feature descriptor. In order to improve the quality of the 3D models, we segmented the plant objects based on the PlantCV platform. We also deducted the optimal number of images needed for reconstructing a high-quality model. Experiments showed that an accurate 3D model of the plant was successfully could be reconstructed by our approach. This 3D surface model reconstruction system provides a simple and accurate computational platform for non-destructive, plant phenotyping.

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“…However, some of the studies simply used nominal values of IOPs provided by the sensor manufacturer in their system calibration procedures, without any correction [17]. Also, other studies did not consider some IOPs such as range distortion parameters ( [17,21,[24][25][26]) and focal length ( [3]) in their self-calibration procedures. To minimize the effect of the self-calibration quality on the accuracy of system calibration and also the quality of RGB-D data produced, the proposed study considers all the involved sensors and their IOPs.…”

Section: Related Workmentioning

confidence: 99%

“…Two or More Columns 2 8 cases (e.g., dataset 5, and 6) 4 cases (e.g., dataset 2, and 9) 3 5 cases (e.g., dataset 5, 6, and 7) 3 cases (e.g., dataset 2, 5, and 9) 4 2 cases (e.g., dataset 1, 2, 3, and 4) 7 cases (e.g., dataset 2, 3, 9, and 10) 5 -6 cases (e.g., dataset 2, 3, 5, 9, and 10) 6 -5 cases (e.g., dataset 2, 3, 5, 6, 9, and 10) 8 -3 cases (e.g., dataset 2, 3, 4, 5, 6, 9, 10, and 11) 9 -2 cases (e.g., dataset 2, 3, 4, 5, 6, 7, 9, 10, and 11)…”

Section: Number Of Datasets Location Of Selected Datasets One Columnmentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive Analysis of System Calibration between Optical Camera and Range Finder

Choi

Kim

2018

IJGI

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This paper describes the comprehensive analysis of system calibration between an optical camera and a range finder. The results suggest guidelines for accurate and efficient system calibration enabling high-quality data fusion. First, self-calibration procedures were carried out using a testbed designed for both the optical camera and range finder. The interior orientation parameters of the utilized sensors were precisely computed. Afterwards, 92 system calibration experiments were carried out according to different approaches and data configurations. For comparison of the various experimental results, two measures, namely the matching rate of fusion data and the standard deviation of relative orientation parameters derived after system calibration procedures, were considered. Among the 92 experimental cases, the best result (the matching rate of 99.08%) was shown for the use of the one-step system calibration method and six datasets from multiple columns. Also, the root mean square values of the residuals after the self-and system calibrations were less than 0.8 and 0.6 pixels, respectively. In an overall evaluation, it was confirmed that the one-step system calibration method using four or more datasets provided more stable and accurate relative orientation parameters and data fusion results than the other cases.

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Time-of-flight sensor and color camera calibration for multi-view acquisition

Cited by 26 publications

References 4 publications

Split and merge approach for detecting multiple planes in a depth image

Split and merge approach for detecting multiple planes in a depth image

Novel Low Cost 3D Surface Model Reconstruction System for Plant Phenotyping

Comprehensive Analysis of System Calibration between Optical Camera and Range Finder

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