Beyond Checkerboards: Advantages of Photogrammetric Camera Calibration for Robust 3D Vision and Novel-View Generation

Imagery is fundamental to modern scientific research, making robust intrinsic camera calibration indispensable for accurate visual inference. The checkerboard-based calibration method has long been favored for its simplicity and ease of deployment and is widely used even in mission-critical computer vision pipelines. However, its limitations in modeling high-precision camera geometry can compromise downstream performance in tasks requiring geometric accuracy. In this work, camera calibration is revisited through the lens of photogrammetric self-calibration (PSC), and it is demonstrated that the PSC consistently outperforms the checkerboard method in both accuracy and precision across a range of vision tasks, including 3D reconstruction with structure from motion (SfM), visual simultaneous localization and mapping (SLAM), and novel-view synthesis and reconstruction. Our findings advocate for a paradigm shift toward calibration methods that better reflect the physical and projective properties of camera systems in real-world deployments for critical computer vision applications.