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Sebastian Schmidt

5 accepted papers

2025

GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation

Phillip Mueller, Talip Uenlue, Sebastian Schmidt, Marcel Kollovieh, Jiajie Fan, Stephan Günnemann +1
ICCV 2025poster

Precise geometric control in image generation is essential for fields like engineering & product design and creative industries to control 3D object features accurately in 2D image space. Traditional 3D editing approaches are time-consuming and demand specialized skills, while current image-based ge…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Joint Out-of-Distribution Filtering and Data Discovery Active Learning

Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann
CVPR 2025poster

As the data demand for deep learning models increases, active learning (AL) becomes essential to strategically select samples for labeling, which maximizes data efficiency and reduces training costs. Real-world scenarios necessitate the consideration of incomplete data knowledge within AL. Prior wor…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Prior2Former - Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentation

Sebastian Schmidt, Julius Koerner, Dominik Fuchsgruber, Stefano Gasperini, Federico Tombari, Stephan Günnemann
ICCV 2025poster

In panoptic segmentation, individual instances must be separated within semantic classes. As state-of-the-art methods rely on a pre-defined set of classes, they struggle with novel categories and out-of-distribution (OOD) data. This is particularly problematic in safety-critical applications, such a…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

Deep Sensor Fusion with Constraint Safety Bounds for High Precision Localization

Sebastian Schmidt, Ludwig Stumpp, Diego Valverde, Stephan Günnemann
IROS 2024poster

In mobile robotics, particularly in autonomous driving, localization is one of the key challenges for navigation and planning. For safe operation in the open world where vulnerable participants are present, precise and guaranteed safe localization is required. While current classical fusion approach…

Cited by 2Source ↗Scholar ↗
2024

Generalized Synchronized Active Learning for Multi-Agent-Based Data Selection on Mobile Robotic Systems

Sebastian Schmidt, Lukas Stappen, Leo Schwinn, Stephan Günnemann
RA-L 2024

In mobile robotics, perception in uncontrolled environments like autonomous driving is a central hurdle. Existing active learning frameworks can help enhance perception by efficiently selecting data samples for labeling, but they are often constrained by the necessity of full data availability in da

Cited by 6Source ↗Scholar ↗