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Ipek Oguz

4 accepted papers

2026

ProbeMDE: Uncertainty-Guided Active Proprioception for Monocular Depth Estimation in Surgical Robotics

ICRA 2026poster

Monocular depth estimation (MDE) provides a useful tool for robotic perception, but its predictions are often uncertain and inaccurate in challenging environments such as surgical scenes where textureless surfaces, specular reflections, and occlusions are common. To address this, we propose ProbeMDE…

2025

From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction

IROS 2025

Surgical automation requires precise guidance and understanding of the scene. Current methods in the literature rely on bulky depth cameras to create maps of the anatomy; however, this does not translate well to space-limited clinical applications. Monocular cameras are small and allow minimally inv

Cited by 6SourceScholar
2025

Scaling Data-Driven Probabilistic Robustness Analysis for Semantic Segmentation Neural Networks

NeurIPS 2025poster

Semantic segmentation neural networks (SSNs) are increasingly essential in high-stakes fields such as medical imaging, autonomous driving, and environmental monitoring, where robustness to input uncertainties and adversarial examples is crucial for ensuring safety and reliability. However, tradition…

Cited by 0SourceScholar
2017

Efficient Optimization for Hierarchically-structured Interacting Segments (HINTS)

CVPR 2017poster

We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments. Any given tree can specify a partial order over object labels defining a hierarchy. It is well-established that segment interactions, such as inclusion/exclusion…

Cited by 13PDFScholar