Wet filament winding has been established as the primary manufacturing process for overwrapped pressure vessels. However, the prediction of the mechanical performance of pressure vessels is still not sufficiently accurate, mostly due to process‐induced deviations within the laminate structure. In this work, we present an image processing algorithm (IPA‐Delfin) that enables the real‐time generation of a digital reconstruction based on the actual geometry and position of the placed roving. A Type‐IV pressure vessel is analyzed under real manufacturing conditions. Our approach allows the measurement of the overlapping degree of roving within the laminate and the prediction of gaps. The results show that the fiber‐band width and the overlapping area vary significantly when the roving is placed directly on the vessel's polymer liner. A more constant and uniform fiber‐band width and overlapping area were observed when the filament winding took place on the previously placed roving. Hence, the IPA‐Delfin can be potentially used for the generation of digital twins of composite overwrapped pressure vessels.

Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume.Transforming the raw point cloud data into a dense 2D matrix structure is a promising way for applying compression algorithms. We propose a new lossless and calibrated 3D-to-2D transformation which allows compression algorithms to efficiently exploit spatial correlations within the 2D representation. To compress the structured representation, we use common image compression methods and also a self-supervised deep compression approach using a recurrent neural network. We also rearrange the LiDAR's intensity measurements to a dense 2D representation and propose a new metric to evaluate the compression performance of the intensity. Compared to approaches that are based on generic octree point cloud compression or based on raw point cloud data compression, our approach achieves the best quantitative and visual performance. Source code and dataset are available at https://github. com/ika-rwth-aachen/Point-Cloud-Compression.