# BabelCalib: A Universal Approach to Calibrating Central Cameras

arXiv: https://arxiv.org/abs/2109.09704 · HTML: https://mlsanity.com/paper/2109.09704/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 45 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018)
- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve](https://mlsanity.com/paper/1912.02908/) (2019)
- [Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration](https://mlsanity.com/paper/2011.08988/) (2020)
- [Minimal Solvers for Rectifying from Radially-Distorted Conjugate Translations](https://mlsanity.com/paper/1911.01507/) (2019)

## Built on by (most cited first, 14 of 14)

- [GeoCalib: Learning Single-image Calibration with Geometric Optimization](https://mlsanity.com/paper/2409.06704/) (2024)
- [AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration](https://mlsanity.com/paper/2503.12701/) (2025)
- [Camera Calibration using a Collimator System](https://mlsanity.com/paper/2409.20034/) (2024)
- [Neural Lens Modeling](https://mlsanity.com/paper/2304.04848/) (2023)
- [Wid3R: Wide Field-of-View 3D Reconstruction via Camera Model Conditioning](https://mlsanity.com/paper/2602.05321/) (2026)
- [Accurate Pose Estimation for Flight Platforms based on Divergent Multi-Aperture Imaging System](https://mlsanity.com/paper/2502.19708/) (2025)
- [InFlux: A Benchmark for Self-Calibration of Dynamic Intrinsics of Video Cameras](https://mlsanity.com/paper/2510.23589/) (2025)
- [CalibAnyView: Beyond Single-View Camera Calibration in the Wild](https://mlsanity.com/paper/2605.14615/) (2026)
- [TartanCalib: Iterative Wide-Angle Lens Calibration using Adaptive SubPixel Refinement of AprilTags](https://mlsanity.com/paper/2210.02511/) (2022)
- [Generic Calibration: Pose Ambiguity/Linear Solution and Parametric-hybrid Pipeline](https://mlsanity.com/paper/2508.07217/) (2025)
- [Flexible Camera Calibration using a Collimator System](https://mlsanity.com/paper/2512.16113/) (2025)
- [Implicit Multi-Camera System Calibration Using Gaussian Processes](https://mlsanity.com/paper/2605.07491/) (2026)
- [Automatic Reproducible Camera Intrinsic Calibration](https://mlsanity.com/paper/2609.10082/) (2026)
- [Many Eyes, One World: Feed-Forward 3D Reconstruction from Mixed Cameras](https://mlsanity.com/paper/2609.35658/) (2026)

## Abstract (arXiv)

Existing calibration methods occasionally fail for large field-of-view cameras due to the non-linearity of the underlying problem and the lack of good initial values for all parameters of the used camera model. This might occur because a simpler projection model is assumed in an initial step, or a poor initial guess for the internal parameters is pre-defined. A lot of the difficulties of general camera calibration lie in the use of a forward projection model. We side-step these challenges by first proposing a solver to calibrate the parameters in terms of a back-projection model and then regress the parameters for a target forward model. These steps are incorporated in a robust estimation framework to cope with outlying detections. Extensive experiments demonstrate that our approach is very reliable and returns the most accurate calibration parameters as measured on the downstream task of absolute pose estimation on test sets. The code is released at https://github.com/ylochman/babelcalib.