TY - GEN A1 - Eltaher, Mahmoud A1 - Bonhage, Alexander A1 - Raab, Thomas A1 - Breuß, Michael T1 - A modified Mask R-CNN approach for automated detection of archaeological sites on high resolution LiDAR-derived DEMs T2 - Anthropogenetische Geomorphologie - Geomorophologie im Anthropozän : Virtuelle Jahrestagung des Arbeitskreises für Geomorphologie 2020, 28./29. September 2020, BTU Cottbus - Senftenberg KW - Anthropogenetische Geomorphologie; Anthropozän; BTU; Geopedologie Anthropocene; Anthropogenic geomorphology; Geopedology Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-53633 SN - 2196-4122 SP - 50 PB - Cottbus ER - TY - GEN A1 - Bonhage, Alexander A1 - Eltaher, Mahmoud A1 - Raab, Thomas A1 - Breuß, Michael A1 - Raab, Alexandra A1 - Schneider, Anna T1 - A modified Mask region‐based convolutional neural network approach for the automated detection of archaeological sites on high‐resolution light detection and ranging‐derived digital elevation models in the North German Lowland T2 - Archaeological Prospection Y1 - 2021 U6 - https://doi.org/10.1002/arp.1806 SN - 1099-0763 VL - 28 IS - 2 SP - 177 EP - 186 ER - TY - GEN A1 - Eltaher, Mahmoud A1 - Breuß, Michael T1 - Unsupervised Description of 3D Shapes by Superquadrics Using Deep Learning T2 - Computer Vision and Machine Intelligence : Proceedings of CVMI 2022 N2 - The decomposition of 3D shapes into simple yet representative components is a very intriguing topic in computer vision as it is very useful for many possible applications. Superquadrics may be used with benefit to obtain an implicit representation of the 3D shapes, as they allow to represent a wide range of possible forms by few parameters. However, in the computation of the shape representation, there is often an intricate trade-off between the variation of the represented geometric forms and the accuracy in such implicit approaches. In this paper, we propose an improved loss function, and we introduce beneficial computational techniques. By comparing results obtained by our new technique to the baseline method, we demonstrate that our results are more reliable and accurate, as well as much faster to obtain. KW - Implicit shape representation Superquadrics Deep learning 3D shape description Y1 - 2023 SN - 978-981-19-7866-1 U6 - https://doi.org/10.1007/978-981-19-7867-8_9 SN - 978-981-19-7867-8 SP - 95 EP - 107 PB - Springer CY - Singapore ER - TY - GEN A1 - Eltaher, Mahmoud A1 - Breuß, Michael T1 - Deep learning for unsupervised 3D shape representation with superquadrics T2 - AI N2 - The representation of 3D shapes from point clouds remains a fundamental challenge in computer vision. A common approach decomposes 3D objects into interpretable geometric primitives, enabling compact, structured, and efficient representations. Building upon prior frameworks, this study introduces an enhanced unsupervised deep learning approach for 3D shape representation using superquadrics. The proposed framework fits a set of superquadric primitives to 3D objects through a fully integrated, differentiable pipeline that enables efficient optimization and parameter learning, directly extracting geometric structure from 3D point clouds without requiring ground-truth segmentation labels. This work introduces three key advancements that substantially improve representation quality, interpretability, and evaluation rigor: (1) A uniform sampling strategy that enhances training stability compared with random sampling used in earlier models; (2) An overlapping loss that penalizes intersections between primitives, reducing redundancy and improving reconstruction coherence; and (3) A novel evaluation framework comprising Primitive Accuracy, Structural Accuracy, and Overlapping Percentage metrics. This new metric design transitions from point-based to structure-aware assessment, enabling fairer and more interpretable comparison across primitive-based models. Comprehensive evaluations on benchmark 3D shape datasets demonstrate that the proposed modifications yield coherent, compact, and semantically consistent shape representations, establishing a robust foundation for interpretable and quantitative evaluation in primitive-based 3D reconstruction. KW - 3D shape representation KW - Superquadrics KW - Deep learning KW - Unsupervised learning KW - Point clouds KW - Geometric modeling Y1 - 2025 U6 - https://doi.org/10.3390/ai6120317 SN - 2673-2688 VL - 6 IS - 12 SP - 1 EP - 36 PB - MDPI AG CY - Basel ER -