Systematic Evaluation of Different Projection Methods for Monocular 3D Human Pose Estimation on Heavily Distorted Fisheye Images

Authors: Stephanie Käs, Sven Peter, Henrik Thillmann, Anton Burenko, Timm Linder, David Adrian, and Dennis Mack, Bastian Leibe

In this work, we tackle the challenge of 3D human pose estimation in fisheye images, which is crucial for applications in robotics, human-robot interaction, and automotive perception. Fisheye cameras offer a wider field of view, but their distortions make pose estimation difficult. We systematically analyze how different camera models impact prediction accuracy and introduce a strategy to improve pose estimation across diverse viewing conditions.

A key contribution of our work is FISHnCHIPS, a novel dataset featuring 3D human skeleton annotations in fisheye images, including extreme close-ups, ground-mounted cameras, and wide-FOV human poses. To support future research, we will be publicly releasing this dataset.

More details coming soon — stay tuned for the final publication! Looking forward to sharing our findings at ICRA 2025!