Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods

TL;DR

This paper introduces a robust validation technique for extended PnP camera calibration methods that does not require direct camera access, enabling accurate model validation using only 2D-3D correspondences and residual analysis.

Contribution

It proposes a novel residual-based validation approach for camera models derived from 2D-3D correspondences without needing camera access, improving model reliability assessment.

Findings

Effective validation on synthetic data and real scenes.

Ability to distinguish underfitted and overfitted models.

Validated annotations in MegaDepth dataset.

Abstract

A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models' residual variances - however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth.