Automatic Camera Pose Estimation by Key-Point Matching of Reference Objects
Jinchen Zeng (Student TU Delft)
Rick M. Butler (TU Delft - Medical Instruments & Bio-Inspired Technology)
John Van Den Dobbelsteen (TU Delft - Medical Instruments & Bio-Inspired Technology)
Benno Hendriks (TU Delft - Medical Instruments & Bio-Inspired Technology, Philips Research Laboratories)
M. Van der Elst (Reinier de Graaf Gasthuis, TU Delft - Support Biomechanical Engineering)
J.H.G. Dauwels (TU Delft - Signal Processing Systems)
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Abstract
In this paper, we aim to design an automatic camera pose estimation pipeline for clinical spaces such as catheterization laboratories. Our proposed pipeline exploits Scaled-YOLOv4 to detect fixed objects. We adopt the self-supervised key-point detector SuperPoint in combination with SuperGlue, a keypoint matching technique based on graph neural networks. Thus, we match key-points on input images with annotated reference points. Reference points are chosen on fixed objects in the scene, such as corners of door posts or windows. The point-correspondences between the image coordinates and the 3D coordinates are applied to the Perspective-n-Point algorithm to estimate the pose of each camera. Compared with other camera pose estimation methods, the proposed pipeline does not require the construction of 3D point-cloud model of the scene or placing a polyhedron object in the scene before each required calibration. Using videos from real procedures, we show that the pipeline can estimate the camera pose with high accuracy.