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      A system for generating complex physically accurate sensor images for automotive applications

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          Abstract

          We describe an open-source simulator that creates sensor irradiance and sensor images of typical automotive scenes in urban settings. The purpose of the system is to support camera design and testing for automotive applications. The user can specify scene parameters (e.g., scene type, road type, traffic density, time of day) to assemble a large number of random scenes from graphics assets stored in a database. The sensor irradiance is generated using quantitative computer graphics methods, and the sensor images are created using image systems sensor simulation. The synthetic sensor images have pixel level annotations; hence, they can be used to train and evaluate neural networks for imaging tasks, such as object detection and classification. The end-to-end simulation system supports quantitative assessment, from scene to camera to network accuracy, for automotive applications.

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          Domain adaptation from multiple sources via auxiliary classifiers

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            Improving SVM accuracy by training on auxiliary data sources

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              Edge diffraction in Monte Carlo ray tracing

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                Author and article information

                Journal
                12 February 2019
                Article
                1902.04258
                30f9f55c-3dd8-4b52-8ef9-adea578f1a00

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                5 pages, 10 figures, IS&T Electronic Imaging conference 2019
                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

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