Implicit Multi-Camera System Calibration Using Gaussian Processes
This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit calibration methods are constrained by rigid mathematical models and struggle with complex, non-linear distortions from unconventional optics, while existing neural network-based implicit approaches are typically data-hungry and lack inherent uncertainty quantification (UQ). Our GP-based model directly learns the complex, non-linear mapping from 2D image coordinates across all cameras to a 3D world coordinate, completely bypassing time-consuming estimation of explicit intrinsic and extrinsic parameters. Moreover, the inherent UQ is critical f...
| Authors | Ivan De Boi, Bart Ribbens, Veronika Golanova et al. |
|---|---|
| Primary category | cs.CV |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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