Random 2.5D U-net for Fully 3D Segmentation. - Christoph Angermann; Markus Haltmeier (MLMECH/CVII-STENT@MICCAI, 2019)

Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation. - Christoph Angermann; Markus Haltmeier; Ruth Steiger; Sergiy Pereverzyev Jr.; Elke Ruth Gizewski (CoRR, 2019)

PiNet: Deep Structure Learning using Feature Extraction in Trained Projection Space. - Christoph Angermann; Markus Haltmeier (CoRR, 2020)

Deep structure learning using feature extraction in trained projection space. - Christoph Angermann; Markus Haltmeier (Comput. Electr. Eng., 2021)

Machine Learning for Nondestructive Wear Assessment in Large Internal Combustion Engines. - Christoph Angermann; Steinbjörn Jónsson; Markus Haltmeier; Adéla Moravová; Christian Laubichler; Constantin Kiesling; Martin Kober; Wolfgang Fimml (CoRR, 2021)

Unsupervised Joint Image Transfer and Uncertainty Quantification Using Patch Invariant Networks. - Christoph Angermann; Markus Haltmeier; Ahsan Raza Siyal (ECCV Workshops (8), 2022)

Uncertainty-Aware Null Space Networks for Data-Consistent Image Reconstruction. - Christoph Angermann; Simon Göppel; Markus Haltmeier (CoRR, 2023)

Three-dimensional Bone Image Synthesis with Generative Adversarial Networks. - Christoph Angermann; Johannes Bereiter-Payr; Kerstin Stock; Markus Haltmeier; Gerald Degenhart (CoRR, 2023)

Surface topography characterization using a simple optical device and artificial neural networks. - Christoph Angermann; Markus Haltmeier; Christian Laubichler; Steinbjörn Jónsson; Matthias Schwab; Adéla Moravová; Constantin Kiesling; Martin Kober; Wolfgang Fimml (Eng. Appl. Artif. Intell., 2023)

Unsupervised single-shot depth estimation using perceptual reconstruction. - Christoph Angermann; Matthias Schwab; Markus Haltmeier; Christian Laubichler; Steinbjörn Jónsson (Mach. Vis. Appl., 2023)

Correction: Unsupervised single-shot depth estimation using perceptual reconstruction. - Christoph Angermann; Matthias Schwab; Markus Haltmeier; Christian Laubichler; Steinbjörn Jónsson (Mach. Vis. Appl., 2023)

ENGinnSAND: A Reference Dataset for Monocular Depth Prediction of Line Structures. - Hubert Bottesch; Christoph Angermann; Christian Laubichler; Constantin Kiesling; Markus Haltmeier (LOD (2), 2024)