Fast Camera Calibration from Orthographic Views of Rotated Objects

Accurate camera calibration is crucial for high-quality 3D reconstruction in computer vision applications. In industrial measuring scenarios, turntable sequences are often captured using telecentric lenses to overcome the foreshortening effect. While specialized Structure-from-Motion (SfM) solutions exist for orthographic projection, these methods are limited to textured objects. Approaches that leverage the scanned object's silhouette for camera calibration are independent of texture but are often restricted to smooth objects or require non-trivial optimization initializations to converge. In this work, we present a novel silhouette-based approach to estimate the rotation axis of a turntable under orthographic projection, extending the applicability to complex geometries, while requiring little to none parameter adjustments. By identifying the symmetry axis of the object's contour envelope and establishing frontier point correspondences on circular trajectories, we robustly estimate the azimuth and inclination angles of the rotation axis, enabling accurate camera pose computation. We evaluate our approach on synthetic datasets comprising four models with varying characteristics and compare it to a state-of-the-art orthographic SfM method, achieving comparable accuracy, while reducing computational cost 37-fold and eliminating reliance on object texture.