A Synthetic Benchmarking Pipeline to Compare Camera Calibration Algorithms
This work addresses the problem of lacking ground truth datasets for camera calibration evaluation in computer vision, providing a tool for researchers and practitioners, though it is incremental as it builds on existing synthetic data methods.
The paper tackles the challenge of measuring camera calibration accuracy by introducing SynthCal, a synthetic benchmarking pipeline that generates images of calibration patterns to quantify algorithm performance, demonstrating its effectiveness in evaluating various algorithms and patterns.
Accurate camera calibration is crucial for various computer vision applications. However, measuring calibration accuracy in the real world is challenging due to the lack of datasets with ground truth to evaluate them. In this paper, we present SynthCal, a synthetic camera calibration benchmarking pipeline that generates images of calibration patterns to measure and enable accurate quantification of calibration algorithm performance in camera parameter estimation. We present a SynthCal generated calibration dataset with four common patterns, two camera types, and two environments with varying view, distortion, lighting, and noise levels for both monocular and multi-camera systems. The dataset evaluates both single and multi-view calibration algorithms by measuring re-projection and root-mean-square errors for identical patterns and camera settings. Additionally, we analyze the significance of different patterns using different calibration configurations. The experimental results demonstrate the effectiveness of SynthCal in evaluating various calibration algorithms and patterns.