Surface finish classification using depth camera data

Frangež, Valens; Salido-Monzú, David

doi:10.1016/j.autcon.2021.103799

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…6,7 On the other hand, surface roughness is itself also an important characteristic of the scanned objects. [8][9][10][11] Conventional optical techniques for independent roughness measurement such as interferometry or confocal microscopy are usually restricted to lab conditions and small working areas. In many practical applications involving large scenes or surfaces whose roughness needs to be assessed remotely, they are thus not applicable.…”

Section: Introductionmentioning

confidence: 99%

Classification of material and surface roughness using polarimetric multispectral LiDAR

Han¹,

Salido-Monzú²

2023

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

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Multispectral LiDAR is an emerging active remote sensing technique that combines distance and spectroscopy measurements on light reflected from the surface at the respective measurement point. It is known that the reflectance spectrum can be used for material classification. However, the spectrum also depends on other surface parameters, particularly surface roughness. Herein, we propose an extension of multispectral to polarimetric multispectral LiDAR and introduce polarized and unpolarized reflectance spectra as additional features for classifying materials and roughness. We demonstrate the feasibility and the benefit using a bench-top prototype instrument which allows acquiring standard, polarized and unpolarized reflectance spectra, in addition to distance, in 33 spectral channels with 10 nm bandwidth between 580 and 900 nm. We analyze and interpret the raw spectra obtained from measurements on test specimens consisting of five different materials (PE, PVC, PP, sandstone, limestone) with two different levels of surface roughness. Using a linear support vector machine (SVM) we demonstrate the potential of the different features for independent material and roughness classification. The results indicate that the unpolarized reflectance spectrum increases the material classification accuracy by 50% as compared to a standard spectrum, and that the polarized spectrum actually allows classifying roughness. We interpret the results as a strong indication that multispectral polarimetric LiDAR enables deriving practically relevant additional information on surfaces with high spatial resolution through remote sensing.

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Section: Introductionmentioning

confidence: 99%

Classification of material and surface roughness using polarimetric multispectral LiDAR

Han¹,

Salido-Monzú²

2023

Multimodal Sensing and Artificial Intelligence: Technologies and Applications III

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“…18 The impact of surface roughness weakens accurate material information extraction from the reflectance spectrum. Conversely, surface roughness information of different building components is valuable for quality control in construction 10,19 and degradation assessment of construction materials. 11,20 Therefore, decoupling the impacts of material composition and surface roughness on the reflectance spectrum measured by multispectral LiDAR is highly beneficial to extract rich and accurate material information.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, surface roughness is itself also an important characteristic of the scanned objects 8 – 11 Conventional optical techniques for independent roughness measurement, such as interferometry or confocal microscopy, are limited to small working areas. In many practical applications involving large scenes or surfaces whose roughness needs to be assessed remotely, they are thus not applicable 12 .…”

Section: Introductionmentioning

confidence: 99%

Classification of material and surface roughness using polarimetric multispectral LiDAR

Han,

Salido-Monzú,

Wieser

2023

Opt. Eng.

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“…Given the typical measurement ranges up to a few meters and required accuracies at the mm level, the latter is driving a new generation of ToF cameras (see e.g. Helios Lucid [8]) with increased potential in new application domains such as, e.g., geodetic and industrial metrology [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Assessment and Improvement of Distance Measurement Accuracy for Time-of-Flight Cameras

Frangež¹,

Salido-Monzú²

2022

IEEE Trans. Instrum. Meas.

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Time-of-flight depth cameras are interesting sensors for contact-less 3D metrology because they combine mechanical robustness with independence of ambient lighting conditions. Their actual performance depends on many factors and is hard to predict from data sheets. In this study we investigate the deviations of the distance measurements of a high-end phasebased depth camera. We focus on the impact of (i) self-warming and external temperature, (ii) on range noise as a function of distance and acquisition time, and (iii) on distance-dependent biases. We present the dedicated experimental setups comprising a climate chamber, a calibration bench with a reference interferometer, and a laser tracker that provide controlled conditions and ground truth data. These setups allow investigating the absolute accuracy and mitigating repeatable distance biases by adapting the measurement model based on experimental data. For demonstration, we apply the investigation to two stateof-the-art industrial depth cameras of the same brand and type (Helios Lucid), showing significantly different response to external temperature but similar distance-dependent biases. We adapt the measurement model of one of the cameras for distancedependent inter-pixel biases and demonstrate that the resulting parameters reduce also the distance biases of the other camera by about 80 % to less than 1 mm at ranges of up to 1 m. This indicates the potential for batch error compensation. The paper contributes to better understanding distance deviations of depth cameras and to improving the accuracy of such cameras.

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Surface finish classification using depth camera data

Cited by 8 publications

References 22 publications

Classification of material and surface roughness using polarimetric multispectral LiDAR

Classification of material and surface roughness using polarimetric multispectral LiDAR

Classification of material and surface roughness using polarimetric multispectral LiDAR

Assessment and Improvement of Distance Measurement Accuracy for Time-of-Flight Cameras

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