Accurate Range Image Generation Using Sensor Fusion of TOF and Stereo-basedMeasurement

Fukuda, Tomoaki; Ji, Yonghoon; Umeda, and Kazunori

doi:10.1109/mecatronics.2018.8495739

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Second, the acquired raw 3D data lack true color information, which is not conducive to research on the color characteristics of a plant canopy. This limitation can be addressed using multi-sensor fusion methods [ 60 , 61 ], such as the combination of 3D point clouds with color information through the registration of a coordinate system between depth sensors and the visible light imaging sensor [ 62 ]. Third, the quality of the point cloud and the 3D reconstruction effect is affected by manual scanning operations; redundant points and noise points will increase as the same area is scanned repeatedly, which will occur if the scanning effect of a certain area of a maize plant is not ideal.…”

Section: Discussionmentioning

confidence: 99%

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

Zhu

Guan

et al. 2019

Sensors

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A reasonable plant type is an essential factor for improving canopy structure, ensuring a reasonable expansion of the leaf area index and obtaining a high-quality spatial distribution of light. It is of great significance in promoting effective selection of the ecological breeding index and production practices for maize. In this study, a method for calculating the phenotypic traits of the maize canopy in three-dimensional (3D) space was proposed, focusing on the problems existing in traditional measurement methods in maize morphological structure research, such as their complex procedures and relatively large error margins. Specifically, the whole maize plant was first scanned with a FastSCAN hand-held scanner to obtain 3D point cloud data for maize. Subsequently, the raw point clouds were simplified by the grid method, and the effect of noise on the quality of the point clouds in maize canopies was further denoised by bilateral filtering. In the last step, the 3D structure of the maize canopy was reconstructed. In accordance with the 3D reconstruction of the maize canopy, the phenotypic traits of the maize canopy, such as plant height, stem diameter and canopy breadth, were calculated by means of a fitting sphere and a fitting cylinder. Thereafter, multiple regression analysis was carried out, focusing on the calculated data and the actual measured data to verify the accuracy of the calculation method proposed in this study. The corresponding results showed that the calculated values of plant height, stem diameter and plant width based on 3D scanning were highly correlated with the actual measured data, and the determinant coefficients R2 were 0.9807, 0.8907 and 0.9562, respectively. In summary, the method proposed in this study can accurately measure the phenotypic traits of maize. Significantly, these research findings provide technical support for further research on the phenotypic traits of other crops and on variety breeding.

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Section: Discussionmentioning

confidence: 99%

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

Zhu

Guan

et al. 2019

Sensors

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“…Presently, a wide variety of studies are being conducted on 3D image sensor technology. Typical 3D imaging methods include time of flight (ToF), structured light, and stereo vision [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

CMOS Depth Image Sensor with Offset Pixel Aperture Using a Back-Side Illumination Structure for Improving Disparity

Lee

Kim

Kwen

et al. 2020

Sensors

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This paper presents a CMOS depth image sensor with offset pixel aperture (OPA) using a back-side illumination structure to improve disparity. The OPA method is an efficient way to obtain depth information with a single image sensor without additional external factors. Two types of apertures (i.e., left-OPA (LOPA) and right-OPA (ROPA)) are applied to pixels. The depth information is obtained from the disparity caused by the phase difference between the LOPA and ROPA images. In a CMOS depth image sensor with OPA, disparity is important information. Improving disparity is an easy way of improving the performance of the CMOS depth image sensor with OPA. Disparity is affected by pixel height. Therefore, this paper compared two CMOS depth image sensors with OPA using front-side illumination (FSI) and back-side illumination (BSI) structures. As FSI and BSI chips are fabricated via different processes, two similar chips were used for measurement by calculating the ratio of the OPA offset to pixel size. Both chips were evaluated for chief ray angle (CRA) and disparity in the same measurement environment. Experimental results were then compared and analyzed for the two CMOS depth image sensors with OPA.

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Experimental Evaluation of Highly Accurate 3D Measurement Using Stereo Camera and Line Laser

Nonaka,

Pathak,

Umeda

2023

J. Robot. Mechatron.

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This paper proposes a method to improve the accuracy of 3D measurement of a stereo camera by marking a measured object using a line laser. Stereo cameras are commonly used for 3D measurement, but the accuracy of 3D measurement is affected by the amount of texture. Therefore, a new measurement system combining a stereo camera and a line laser is developed. The accuracy of 3D measurement with a stereo camera is improved by using a line laser to mark arbitrary points on the measured object and measuring the marked points, regardless of the amount of texture on the measured object. Because the laser is only used to mark points on the measurement target, calibration is not required with the stereo camera. Experimental evaluation showed that our proposed method can obtain millimeters.

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Accurate Range Image Generation Using Sensor Fusion of TOF and Stereo-basedMeasurement

Cited by 3 publications

References 4 publications

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

Calculation Method for Phenotypic Traits Based on the 3D Reconstruction of Maize Canopies

CMOS Depth Image Sensor with Offset Pixel Aperture Using a Back-Side Illumination Structure for Improving Disparity

Experimental Evaluation of Highly Accurate 3D Measurement Using Stereo Camera and Line Laser

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