Petr Kellnhofer

Papers

Total Citations

1,118

H-Index

About

Petr Kellnhofer is a leading researcher at the intersection of computer vision, machine perception, and human-computer interaction. His work is distinguished by a deep focus on understanding and modeling human behavior through novel sensing and computational techniques. His most impactful contribution, the 2019 paper "Learning the signatures of the human grasp using a scalable tactile glove," has garnered over 1,100 citations. This landmark study introduced a scalable tactile-sensing glove and a deep learning framework to capture and interpret the rich, complex patterns of human grasp, effectively translating physical touch into digital data. Beyond this, Kellnhofer has made significant advances in gaze estimation, light transport simulation, and neural rendering, often developing methods that bridge the gap between physical reality and digital representation. His research, frequently published at top venues like CVPR and SIGGRAPH, is characterized by its ambition to build systems that perceive and interact with the world as humans do. For students and researchers, Kellnhofer’s work exemplifies how combining clever hardware design with powerful machine learning can unlock new frontiers in understanding human interaction and perception.