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Thesis defences

Point-Based Real-Time Recalibration for Infrared Multi-Camera Systems

Date & time
Wednesday, June 19, 2024
1:15 p.m. – 3 p.m.

Benyamin Mehmander


This event is free


Department of Computer Science and Software Engineering


ER Building
2155 Guy St.
Room Zoom

Wheel chair accessible



   This thesis addresses the critical challenge of achieving real-time and precise calibration for multi-camera systems, particularly crucial for applications demanding high precision. Departing from conventional methodologies characterized by limited adaptability and incapacity for on-the-fly recalibrations, this research introduces an innovative neural network-based approach designed to facilitate dynamic real-time calibration. This work incorporates the camera pose synthesis pipeline to simulate real-world camera parameters, introducing various levels of perturbation to the camera parameters to enhance model robustness. Also, a differentiable projection model is utilized to establish a direct correlation between 3D geometries and their 2D image projections, thereby facilitating concurrent optimization of intrinsic and extrinsic camera parameters. This solution entails a multi-headed deep neural network regression model and is tailored for multi-camera systems equipped with onboard processing capabilities, leveraging direct 2D projections of 3D fiducials. A comprehensive series of experiments are conducted to demonstrate the superior efficacy of the proposed method over traditional calibration techniques. This research contributes significantly to the advancement of real-time multi-camera system calibration, offering promising implications for diverse domains reliant on precise temporal synchronization and spatial accuracy.

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