Augmented reality applications use object tracking to estimate the pose of a camera and to superimpose virtual content onto the observed object. Today, a number of tracking systems are available, ready to be used in industrial applications. However, such systems are hard to handle for a service maintenance engineer, due to obscure configuration procedures. In this article, we investigate options towards replacing the manual configuration process with a machine learning approach based on automatically synthesized data. We present an automated process of creating object tracker facilities exclusively from synthetic data. The data is highly enhanced to train a convolutional neural network, while still being able to receive reliable and robust results during real world applications only from simple RGB cameras. Comparison against related work using the LINEMOD dataset showed that we are able to outperform similar approaches. For our intended industrial applications with high accuracy demands, its performance is still lower than common object tracking methods with manual configuration. Yet, it can greatly support those as an add-on during initialization, due to its higher reliability.

Abstract If spatial augmented reality is used in the design process of a car, then one of the most important issues is that the virtual content is projected with a very high visual quality onto the real object, because based on this projection design decisions are made. Especially, the visualised colours on the real object should not be distinguishable from corresponding real reference colours. In this paper, we introduce a new approach for the augmentation of real objects which is able to match the requirements of a design process. We present a new rendering method with ray tracing which increases the visual quality of the projection images in comparison to existing methods. The desired values of these images have further to be adjusted according to the material, the ambient light and the local orientation of the projector. For this purpose, we develop a physically based computation which exactly determines the corresponding projection intensities for these values by using three‐dimensional lookup tables at every projector pixel. Since not all of the desired values can be represented with an intensity of the projector, an adjustment has to be computed for these values. Therefore, we conduct a user study with design experts who work in the automotive industry and use the results to propose a new adjustment method for such values. Finally, we compare our methods to existing procedures and conclude which ones are suitable for the design process of a car.