The performance of machine learning and deep learning algorithms for image analysis depends significantly on the quantity and quality of the training data. The generation of annotated training data is often costly, time-consuming and laborious. Data augmentation is a powerful option to overcome these drawbacks. Therefore, we augment training data by rendering images with arbitrary poses from 3D models to increase the quantity of training ...

In this paper we present a system for three-dimensional reconstruction of architectural scenes from uncalibrated videos. These videosmight be recorded using hand-held cameras, downloaded from the internet or taken from archival sources. Because we do not requireprior knowledge of the camera’s internal parameters such as focal length, center of projection, and radial distortion we can deal withvideos from uncontrolled sources. We present ...

Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the reconstruction problem, most, due to their deterministic nature, do not directly address the issue of quantifying uncertainty associated with their predictions. We remedy this by proposing a novel probabilistic deep learning approach capable of simultaneous surface ...