Entwurf Von Computational Imaging Systemen Am Beispiel Der Monokularen Tiefenschaetzung

Entwurf von Computational-Imaging-Systemen am Beispiel der monokularen Tiefenschaetzung

Computational, label, and data efficiency in deep learning for sparse 3D data

Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.

Light Field Imaging for Deflectometry

Optical measurement methods are becoming increasingly important for high-precision production of components and quality assurance. The increasing demand can be met by modern imaging systems with advanced optics, such as light field cameras. This work explores their use in the deflectometric measurement of specular surfaces. It presents improvements in phase unwrapping and calibration techniques, enabling high surface reconstruction accuracies using only a single monocular light field camera.