Modeling the Effects of Windshield Refraction for Camera Calibration

Verbiest, Frank; Proesmans, Marc; Gool, Luc Van

doi:10.1007/978-3-030-58539-6_24

Cited by 14 publications

(37 citation statements)

References 20 publications

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“…Tangential distortion can be also caused when cameras are mounted behind the windshield (e.g. a common position for functions like lane keep assist and sign recognition), that has its own curvature, slightly deflecting incoming light [14], [17], [18]. The developed model for windshield tangential distortion is described in sec.…”

Section: B Related Workmentioning

confidence: 99%

“…Lens wear out and ageing resulting in attenuation and refractions. [17] [28] [29] 08. Vibration of Mounting…”

Section: ✓ ✓mentioning

confidence: 99%

“…Windshields normally come with certain amount of surface curvature and inclination for aerodynamic and safety reasons. The curvature will be usually small, and a tangential distortion model can be used to describe this distortion [17]. In fact, radial distortion can be attributed to stronger distortion due to lens characteristics and misplacement with respect to the image centre, but, as this distortion is normally corrected during the calibration process (by sensor manufacturers), it can be omitted from the model here.…”

Section: A Windshield Tangential Distortion Modelmentioning

confidence: 99%

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Analysis of Automotive Camera Sensor Noise Factors

Li¹,

Chan²,

Baris³

et al. 2022

Preprint

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<p>Assisted and automated driving functions are increasingly deployed to support improved safety, efficiency, and enhance driver experience. However, there are still key technical challenges that need to be overcome, such as the degradation of perception sensor data due to noise factors. The quality of data being generated by sensors can directly impact the planning and control of the vehicle, which can affect the vehicle safety. This work builds on a recently proposed framework, analysing noise factors on automotive LiDAR sensors, and deploys it to camera sensors, focusing on the specific disturbed sensor outputs via a detailed analysis and classification of automotive camera specific noise sources (30 noise factors are identified and classified in this work). Moreover, the noise factor analysis has recognised two omnipresent and independent noise factors (i.e. obstruction and windshield distortion). These noise factors have been modelled to generate noisy camera data; their impact on the perception step, based on deep neural networks, has been evaluated when the noise factors are applied independently and simultaneously. It is demonstrated that the performance degradation from the combination of noise factors is not simply the accumulated performance degradation from each single factor, which raises the importance of including the simultaneous analysis of multiple noise factors. Thus, the framework can support and enhance the use of simulation for development and testing of automated vehicles through careful consideration of the noise factors affecting camera data. </p>

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Section: B Related Workmentioning

confidence: 99%

“…Lens wear out and ageing resulting in attenuation and refractions. [17] [28] [29] 08. Vibration of Mounting…”

Section: ✓ ✓mentioning

confidence: 99%

Section: A Windshield Tangential Distortion Modelmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Automotive Camera Sensor Noise Factors

Li¹,

Chan²,

Baris³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Windshields normally come with certain amount of surface curvature and inclination for aerodynamic and safety reasons. The curvature will be usually small, and a tangential distortion model can be used to describe this distortion [17]. In fact, radial distortion can be attributed to stronger distortion due to lens characteristics and misplacement with respect to the sensor centre, but, as this distortion is normally corrected during the calibration process (by sensor manufacturers), it can be omitted from the model here.…”

Section: A Windshield Tangential Distortion Modelmentioning

confidence: 99%

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

Li¹,

Chan²,

Baris³

et al. 2022

Preprint

View full text Add to dashboard Cite

<p>Assisted and automated driving functions are increasingly deployed to support improved safety, efficiency, and enhance driver experience. However, there are still key technical challenges that need to be overcome, such as the degradation of perception sensor data due to noise factors. The quality of data being generated by sensors can directly impact the planning and control of the vehicle, which can affect the vehicle safety. This work builds on a recently proposed framework, analysing noise factors on automotive LiDAR sensors, and deploys it to camera sensors, focusing on the specific disturbed sensor outputs via a detailed analysis and classification of automotive camera specific noise sources (30 noise factors are identified and classified in this work). Moreover, the noise factor analysis has identified two omnipresent and independent noise factors (i.e. obstruction and windshield distortion). These noise factors have been modelled to generate noisy camera data; their impact on the perception step, based on deep neural networks, has been evaluated when the noise factors are applied independently and simultaneously. It is demonstrated that the performance degradation from the combination of noise factors is not simply the accumulated performance degradation from each single factor, which raises the importance of including the simultaneous analysis of multiple noise factors. Thus, the framework can support and enhance the use of simulation for development and testing of automated vehicles through careful consideration of the noise factors affecting camera data. </p>

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“…The authors report that their calibration approach is very time-consuming and practically limited. Besides underwater imaging, exact modeling of refraction in the image formation process can also improve in-air applications such as cameras behind the windshield of cars [21] or behind an optical cover for coal mining vehicles [22]. Note however, that when imaging in air through a relatively thin glass pane, rays essentially only undergo a slight lateral shift depending on the thickness of the glass, whereas in our scenario the refraction effects are dominated by the other two media (air and water) with significantly different optical densities that cause large direction changes of rays.…”

mentioning

confidence: 99%

Refractive Geometry for Underwater Domes

She,

Nakath,

Song

et al. 2021

Preprint

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Underwater cameras are typically placed behind glass windows to protect them from the water. Spherical glass, a dome port, is well suited for high water pressures at great depth, allows for a large field of view, and avoids refraction if a pinhole camera is positioned exactly at the sphere's center. Adjusting a real lens perfectly to the dome center is a challenging task, both in terms of how to actually guide the centering process (e.g. visual servoing) and how to measure the alignment quality, but also, how to mechanically perform the alignment. Consequently, such systems are prone to being decentered by some offset, leading to challenging refraction patterns at the sphere that invalidate the pinhole camera model. We show that the overall camera system becomes an axial camera, even for thick domes as used for deep sea exploration and provide a noniterative way to compute the center of refraction without requiring knowledge of exact air, glass or water properties. We also analyze the refractive geometry at the sphere, looking at effects such as forward-vs. backward decentering, iso-refraction curves and obtain a 6th-degree polynomial equation for forward projection of 3D points in thin domes. We then propose a pure underwater calibration procedure to estimate the decentering from multiple images. This estimate can either be used during adjustment to guide the mechanical position of the lens, or can be considered in photogrammetric underwater applications.

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Modeling the Effects of Windshield Refraction for Camera Calibration

Cited by 14 publications

References 20 publications

Analysis of Automotive Camera Sensor Noise Factors

Analysis of Automotive Camera Sensor Noise Factors

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

Refractive Geometry for Underwater Domes

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