Rendering Physically Correct Raindrops on Windshields for Robustness Verification of Camera-based Object Recognition

Bernuth, Alexander von; Volk, Georg; Bringmann, Oliver

doi:10.1109/ivs.2018.8500494

Cited by 17 publications

(9 citation statements)

References 20 publications

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“…Garg and Nayar [GN04] applied the photometric method to detect rain streaks and estimate the background layer intensities by averaging intensities of non-rain regions. Barnum [vBVB18] introduced continuous nearest neighbor search based on R-trees to render raindrops.…”

Section: Video Deraining Methodsmentioning

confidence: 99%

Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining

Liang

Qiu

et al. 2019

Computer Graphics Forum

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Single image rain removal is a challenging ill‐posed problem due to various shapes and densities of rain streaks. We present a novel incremental randomly wired network (IRWN) for single image deraining. Different from previous methods, most structures of modules in IRWN are generated by a stochastic network generator based on the random graph theory, which ease the burden of manual design and further help to characterize more complex rain streaks. To decrease network parameters and extract more details efficiently, the image pyramid is fused via the multi‐scale network structure. An incremental rectified loss is proposed to better remove rain streaks in different rain conditions and recover the texture information of target objects. Extensive experiments on synthetic and real‐world datasets demonstrate that the proposed method outperforms the state‐of‐the‐art methods significantly. In addition, an ablation study is conducted to illustrate the improvements obtained by different modules and loss items in IRWN.

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Section: Video Deraining Methodsmentioning

confidence: 99%

Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining

Liang

Qiu

et al. 2019

Computer Graphics Forum

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“…Testing on 450 images, the results show that when introducing raindrops to the scene, measured average precision of the algorithm drops by 1.18% and the overall average accuracy drops by 0.37%. Reference [46] also demonstrates that detection accuracy for smaller objects is more sensitive to obstruction by the raindrops (measured average precision for pedestrians drops by 4%).…”

Section: A Simulation Environments and Test Scenariosmentioning

confidence: 97%

“…Additionally, it is highly unlikely to get exactly the same scenario every time, hence reproducibility is also an issue. Approaches based on simulation environments are a viable solution to test autonomous cars in real-world alike environments where any scenario can be synthetically generated and repeated [46].…”

Section: A Simulation Environments and Test Scenariosmentioning

confidence: 99%

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Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

et al. 2021

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“…Weather Simulation and Datasets Adverse weather simulation techniques have been developed for snowfall [34], rainfall [19,18], blur [28], fog [31,12,43], night driving [44,32], and raindrops on the windshield [47]. Most datasets [43,46,1,54,31,30] are based on Koschmieder's physical model [26].…”

Section: Domain Adaptationmentioning

confidence: 99%

ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media

Shi¹,

Tseng²,

Bijelic³

et al. 2021

Preprint

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Adverse weather conditions, including snow, rain, and fog pose a challenge for both human and computer vision in outdoor scenarios. Handling these environmental conditions is essential for safe decision making, especially in autonomous vehicles, robotics, and drones. Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets. Without training data, let alone paired data, existing autonomous vehicles often limit themselves to good conditions and stop when dense fog or snow is detected. In this work, we tackle the lack of supervised training data by combining synthetic and indirect supervision. We present ZeroScatter, a domain transfer method for converting RGB-only captures taken in adverse weather into clear daytime scenes. ZeroScatter exploits model-based, temporal, multi-view, multi-modal, and adversarial cues in a joint fashion, allowing us to train on unpaired, biased data. We assess the proposed method using real-world captures, and the proposed method outperforms existing monocular de-scattering approaches by 2.8 dB PSNR on controlled fog chamber measurements.

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Rendering Physically Correct Raindrops on Windshields for Robustness Verification of Camera-based Object Recognition

Cited by 17 publications

References 20 publications

Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining

Rain Wiper: An Incremental Randomly Wired Network for Single Image Deraining

Software Verification and Validation of Safe Autonomous Cars: A Systematic Literature Review

ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media

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