Beyond Reprojection Error: Camera Calibration with 3D Targets

2026-08-05

AI Summary

This work proposes a ray-based camera calibration framework tailored for 3D reconstruction, addressing the limitations of traditional reprojection error–based methods that rely on 2D calibration boards and inadequately reflect 3D geometric accuracy. Instead of reprojection error, the approach introduces reconstruction error and intersection error as more representative metrics. It employs a novel icosahedral 3D calibration target and a ring-shaped feature detector, integrated with a generalized distortion model and bootstrapping to refine both intrinsic and extrinsic parameter estimates. Experimental results on synthetic data demonstrate that the proposed method reduces average intersection error by approximately 40%, significantly enhances calibration stability, and validates that ray-level metrics provide a more faithful assessment of 3D reconstruction fidelity compared to conventional approaches.

Abstract

In 3D reconstruction, camera calibration is an essential element for achieving high fidelity and accuracy of the reconstructed geometry. While existing approaches rely upon 2D planar calibration, this work proposes a framework tailored for 3D reconstruction that is based on predicting scene rays, which adds flexibility to the reconstruction pipeline and enables the use of recent advances in camera models. Novel metrics, reconstruction and intersection error, derived from predicted scene rays are employed in combination with a bootstrapping procedure that statistically evaluates different calibration objects and calibration pipelines for both intrinsic and extrinsic camera parameters. The results show that the generalized distortion model more faithfully captures physical camera effects and yields an improvement in calibration accuracy. Reprojection error is shown to be a potentially misleading indicator of 3D accuracy, and the proposed ray-based metrics provide a more holistic assessment. An icosahedron calibration target is designed to enrich calibration information for 3D reconstruction together with a ring-feature-based detector. The icosahedral target yields approximately 40% lower mean intersection and more stable calibration across bootstrap trials on synthetic data, while real-data performance demands very tight fabrication tolerances.

Problem

• camera calibration
• 3D reconstruction
• reprojection error
• calibration accuracy
• 3D targets

Innovation

• scene rays
• 3D calibration target
• intersection error
• generalized distortion model
• bootstrapping evaluation