Olaf Hellwich
Papers
7
Total Citations
107
H-Index
4
About
Olaf Hellwich is a leading figure in computer vision and robotics, whose research bridges the gap between perception and autonomous systems. His work primarily focuses on 3D reconstruction, trajectory prediction, and sensor fusion for mobile platforms. Hellwich has made significant contributions to large-scale 3D modeling, notably through his regularized volumetric fusion framework, which enables accurate, boundless reconstruction from mobile image sensors—a critical advancement for real-time robotic applications. His pioneering efforts in action-based contrastive learning for trajectory prediction (2022, 48 citations) have reshaped how autonomous systems anticipate pedestrian motion in dynamic, first-person views, directly impacting autonomous driving and human-robot interaction. Hellwich also explored practical sensor integration, demonstrating that smartphone sensors can serve as lightweight, cost-effective navigators for unmanned aerial vehicles (2015, 5 citations). His work on compensating multipath errors in Time-of-Flight cameras (2013, 36 citations) further showcases his expertise in improving sensor reliability. With a career marked by innovative algorithms and real-world applications, Hellwich continues to influence the fields of 3D vision and autonomous navigation, inspiring researchers to push the boundaries of perception-driven robotics.
Research Focus
Key Achievements
Top Papers
- 1Action-Based Contrastive Learning for Trajectory Prediction48 citations · 2022
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- 5Recursive Total Variation Filtering Based 3D Fusion4 citations · 2016
- 6Action-based Contrastive Learning for Trajectory Prediction4 citations · 2022
- 7Boundless Reconstruction Using Regularized 3D Fusion2 citations · 2017