Large Area 3D Human Pose Detection Via Stereo Reconstruction in Panoramic Cameras
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Abstract (arXiv)
We propose a novel 3D human pose detector using two panoramic cameras. We show that transforming fisheye perspectives to rectilinear views allows a direct application of two-dimensional deep-learning pose estimation methods, without the explicit need for a costly re-training step to compensate for fisheye image distortions. By utilizing panoramic cameras, our method is capable of accurately estimating human poses over a large field of view. This renders our method suitable for ergonomic analyses and other pose based assessments.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jul 2019.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2023Human Pose Estimation in Monocular Omnidirectional Top-View Imagescited by 3
- 2023Applications of Deep Learning for Top-View Omnidirectional Imaging: A Surveycited by 3
- 2024NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Human Pose Estimation in Top-View Fisheye Imagescited by 2
- 2025Towards Adaptive Fusion of Multimodal Deep Networks for Human Action Recognitioncited by 0
- 2025Towards Adaptive Fusion of Multimodal Deep Networks for Human Action Recognition4 Dec 2025
- 2024NToP: NeRF-Powered Large-scale Dataset Generation for 2D and 3D Human Pose Estimation in Top-View Fisheye Images28 Feb 2024
- 2023Applications of Deep Learning for Top-View Omnidirectional Imaging: A Survey17 Apr 2023
- 2023Human Pose Estimation in Monocular Omnidirectional Top-View Images17 Apr 2023