Clara Callenberg

M.Sc. Clara Callenberg

PhD Researcher
E-mail:
Phone: +49 228 73-60636
Room 3.008
Friedrich-Hirzebruch-Allee 8,
D-53115 Bonn
Germany

Publications:

Super-Resolution Time-Resolved Imaging Using Computational Sensor Fusion

Clara Callenberg, Ashley Lyons, Dennis den Brok, Areeba Fatima, Alejandro Turpin, Vytautas Zickus, Laura M. Machesky, Jamie A. Whitelaw, Daniele Faccio, Matthias B. Hullin
Scientific Reports (Nature Publishing Group) 11, 1689 (2021), https://doi.org/10.1038/s41598-021-81159-x, 2021. Super-Resolution Time-Resolved Imaging Using Computational Sensor Fusion

Low-Cost SPAD Sensing for Non-Line-Of-Sight Tracking, Material Classification and Depth Imaging

Clara Callenberg, Zheng Shi, Felix Heide, Matthias B. Hullin
ACM Transactions on Graphics 40 (4), Article 61 (Proc. SIGGRAPH 2021), 2021. Low-Cost SPAD Sensing for Non-Line-Of-Sight Tracking, Material Classification and Depth Imaging

Trigonometric moments for editable structured light range finding

Sebastian Werner, Julian Iseringhausen, Clara Callenberg, Matthias B. Hullin
Proc. Vision, Modeling and Visualization, Rostock, Germany , 2019. Trigonometric moments for editable structured light range finding

We enhance existing structured light phase shifting methods by using trigonometric moments.

Snapshot Difference Imaging using Correlation Time-of-Flight Sensors

Clara Callenberg, Felix Heide, Gordon Wetzstein, Matthias Hullin
ACM Transactions on Graphics 36(6) (Proc. SIGGRAPH Asia), 220:1--220:10, 2017. Snapshot Difference Imaging using Correlation Time-of-Flight Sensors

Computation of image differences is a key operation in computational imaging. We use time-of-flight sensors to perform this operation in a single shot, and discover some remarkable features.

Material Classification using Raw Time-of-Flight Measurements

Shuochen Su, Felix Heide, Robin Swanson, Jonathan Klein, Clara Callenberg, Matthias B. Hullin, Wolfgang Heidrich
Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. Material Classification using Raw Time-of-Flight Measurements

We show that using multi-frequency time-of-flight measurements, five different white materials can be distinguished on a per-pixel basis.