4 papers
Object Detection Benchmarks are Incomplete: The Role of Label Errors and Annotation Uncertainty
Sarina Penquitt, Jonathan Klees, Antonia van Betteray +4
While object detection has advanced through improved architectures and open-vocabulary models, we provide strong evidence that benchmark quality is limited by annotation incomplete…
Predictive Photometric Uncertainty in Gaussian Splatting for Novel View Synthesis
Chamuditha Jayanga Galappaththige, Thomas Gottwald, Peter Stehr +4
Recent advances in 3D Gaussian Splatting have enabled impressive photorealistic novel view synthesis. However, to transition from a pure rendering engine to a reliable spatial map…
Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs
Jonathan Klees, Tobias Riedlinger, Peter Stehr +3
Safe artificial intelligence for perception tasks remains a major challenge, partly due to the lack of data with high-quality labels. Annotations themselves are subject to aleatori…
PRIMU: Uncertainty Estimation for Novel Views in Gaussian Splatting from Primitive-Based Representations of Error and Coverage
Thomas Gottwald, Edgar Heinert, Peter Stehr +2
We introduce Primitive-based Representations of Uncertainty (PRIMU), a post-hoc uncertainty estimation (UE) framework for Gaussian Splatting (GS). Reliable UE is essential for depl…