Bibliographic Details
| Title: |
CameraVQ: Vector‐Quantized Representations for Monocular Camera Calibration. |
| Authors: |
Cheong, DaEun1 (AUTHOR), Han, JungHyun1 (AUTHOR) jhan@korea.ac.kr |
| Source: |
Computer Animation & Virtual Worlds. May/Jun2026, Vol. 37 Issue 3, p1-9. 9p. |
| Subjects: |
Camera calibration, Vector quantization, Machine learning, Computer vision, Image processing, Calibration |
| Abstract: |
We present a monocular camera calibration method, dubbed CameraVQ. It reformulates monocular camera calibration as classification over vector‐quantized camera intrinsics. Existing methods based on geometric cues or direct regression often suffer from unstable optimization and poor generalization. In contrast, CameraVQ learns a discrete codebook of camera intrinsics and predicts the latent code from a single image. This discrete formulation constrains predictions to a statistically learned manifold of valid configurations, enabling robust calibration and strong generalization. Through extensive evaluation on diverse calibration benchmarks, CameraVQ achieves state‐of‐the‐art performance on the majority of datasets. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
