A. Kuijper

Applications of Neural Cellular Automata: State of the Art, Challenges and Opportunities

Nick Lemke, Niklas Ihm, John Kalkhof +11

Neural Cellular Automata (NCAs) are a new type of neural network architecture which enable accurate and robust inference at extremely small model sizes. Recently, NCAs have advance…

AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editing

Saptarshi Neil Sinha, Mika Silvan Goschke, Paul Julius Kühn +2

Computer-aided design is central to industrial manufacturing, and much of a designer's daily work consists of editing existing models from multimodal requests involving speech, ske…

TinyDETR-Pose: Towards End-to-End Real-Time Single-Stage 6DoF Object Pose Estimation with Lightweight Transformers

Paul Julius Kühn, Duc Anh Nguyen, Saptarshi Neil Sinha +2

Real-time 6DoF object pose estimation on resource-constrained hardware remains challenging, as accurate correspondence-based and refinement pipelines typically rely on non-differen…

ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection

Paul Julius Kühn, Saptarshi Neil Sinha, Tiago Kleist +3

While automated defect detection such as the detection of surface scratched is an important aspect in industrial quality control, the scarcity of annotated defect data make this ta…

Beyond Reprojection Error: Camera Calibration with 3D Targets

Dennis Ruppel, Hasan Kutlu, Kai A. Neumann +4

In 3D reconstruction, camera calibration is an essential element for achieving high fidelity and accuracy of the reconstructed geometry. While existing approaches rely upon 2D plan…

SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

Paul Julius Kühn, Mika Pommeranz, Arjan Kuijper +1

Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, and collecting balanced training sets is sl…

Optimizing Multi-Modal Models for Image-Based Shape Retrieval: The Role of Pre-Alignment and Hard Contrastive Learning

Paul Julius Kühn, Cedric Spengler, Michael Weinmann +2

Image-based shape retrieval (IBSR) aims to retrieve 3D models from a database given a query image, hence addressing a classical task in computer vision, computer graphics, and robo…

RangeSAM: On the Potential of Visual Foundation Models for Range-View represented LiDAR segmentation

Paul Julius Kühn, Duc Anh Nguyen, Arjan Kuijper +1

Point cloud segmentation is central to autonomous driving and 3D scene understanding. While voxel- and point-based methods dominate recent research due to their compatibility with…

Neural Restoration of Greening Defects in Historical Autochrome Photographs Based on Purely Synthetic Data

Saptarshi Neil Sinha, P. Julius Kuehn, Johannes Koppe +2

The preservation of early visual arts, particularly color photographs, is challenged by deterioration caused by aging and improper storage, leading to issues like blurring, scratch…

Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning

Annalena Blänsdorf, Tristan Wirth, Arne Rak +3

Segmenting transparent structures in images is challenging since they are difficult to distinguish from the background. Common examples are drinking glasses, which are a ubiquitous…

Improving 6D Object Pose Estimation of metallic Household and Industry Objects

Thomas Pöllabauer, Michael Gasser, Tristan Wirth +3

6D object pose estimation suffers from reduced accuracy when applied to metallic objects. We set out to improve the state-of-the-art by addressing challenges such as reflections an…

EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation

Zixuan Fang, Thomas Pöllabauer, Tristan Wirth +3

In industrial applications requiring real-time feedback, such as quality control and robotic manipulation, the demand for high-speed and accurate pose estimation remains critical.…

eess.IV2024★ 6 cited

Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation

Tillmann Rheude, Andreas Wirtz, Arjan Kuijper +1

Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understandi…

End-to-End Probabilistic Geometry-Guided Regression for 6DoF Object Pose Estimation

Thomas Pöllabauer, Jiayin Li, Volker Knauthe +2

6D object pose estimation is the problem of identifying the position and orientation of an object relative to a chosen coordinate system, which is a core technology for modern XR a…

Transparency Distortion Robustness for SOTA Image Segmentation Tasks

Volker Knauthe, Arne Rak, Tristan Wirth +4

Semantic Image Segmentation facilitates a multitude of real-world applications ranging from autonomous driving over industrial process supervision to vision aids for human beings.…

Influence of Water Droplet Contamination for Transparency Segmentation

Volker Knauthe, Paul Weitz, Thomas Pöllabauer +4

Computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. W…

Fast Training Data Acquisition for Object Detection and Segmentation using Black Screen Luminance Keying

Thomas Pöllabauer, Volker Knauthe, André Boller +2

Deep Neural Networks (DNNs) require large amounts of annotated training data for a good performance. Often this data is generated using manual labeling (error-prone and time-consum…