Six-dimensional object detection of rigid objects is a problem especially relevant for quality control and robotic manipulation in industrial contexts. This work is a survey of the state of the art of 6D object detection with these use cases in mind, specifically focusing on algorithms trained only with 3D models or renderings thereof. Our first contribution is a listing of requirements typically encountered in industrial applications. The second contribution is a collection of quantitative evaluation results for several different 6D object detection methods trained with synthetic data and the comparison and analysis thereof. We identify the top methods for individual requirements that industrial applications have for object detectors, but find that a lack of comparable data prevents large-scale comparison over multiple aspects.

Fraunhofer IGD (a) Calibrated camera matrix only (b) Rectified image (c) Fully calibrated camera Figure 1: Effect of camera calibration on an augmented reality scene: Although a calibrated camera matrix is used in (a), the misalignment is clearly visible. Using a complete distortion model allows rectifying the image (b). Together with an adapted camera matrix, this results in a fully aligned augmentation (c).ABSTRACT For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it.While calibration data is available on some devices through Augmented Reality (AR) frameworks like ARCore and ARKit, for most cameras this information is not available. Therefore, we propose a web based calibration service that not only aggregates calibration data, but also allows calibrating new cameras on-the-fly. We build upon a novel camera calibration framework that enables even novice users to perform a precise camera calibration in about 2 minutes. This allows general deployment of computer vision algorithms on the web, which was previously not possible due to lack of calibration data.

No abstract