Pranav Venkatesan

June 6th, 2025 – 12:30-1:00pm, EC4-2101A

Camera and projector calibration is essential for accurate spatial measurement, particularly in applications like projection mapping where multiple projectors must align precisely on complex surfaces. Since projector calibration involves nonlinear relationships between parameters, nonlinear optimization is required, typically using reprojection error as the objective function. However, in the absence of ground truth, reprojection error alone can result in visible gaps between overlapping projector point clouds. To address this, the thesis proposes a multi-step optimization with a novel objective function. Tested on both simulated and real-world setups, the method shows fast and reliable performance. The optimization parameterizes calibration as a function of stereo overlap, which affects both accuracy and system cost. Understanding this relationship allows reduced overlap without compromising accuracy, minimizing camera usage and cost.