# Why Having 10,000 Parameters in Your Camera Model is Better Than Twelve

arXiv: https://arxiv.org/abs/1912.02908 · HTML: https://mlsanity.com/paper/1912.02908/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 30 of 43)

- [Advances in Neural Rendering](https://mlsanity.com/paper/2111.05849/) (2021)
- [MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors](https://mlsanity.com/paper/2412.12392/) (2024)
- [Self-Calibrating Neural Radiance Fields](https://mlsanity.com/paper/2108.13826/) (2021)
- [Cameras as Rays: Pose Estimation via Ray Diffusion](https://mlsanity.com/paper/2402.14817/) (2024)
- [CamP: Camera Preconditioning for Neural Radiance Fields](https://mlsanity.com/paper/2308.10902/) (2023)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration](https://mlsanity.com/paper/2503.12701/) (2025)
- [BabelCalib: A Universal Approach to Calibrating Central Cameras](https://mlsanity.com/paper/2109.09704/) (2021)
- [Multi-sensor large-scale dataset for multi-view 3D reconstruction](https://mlsanity.com/paper/2203.06111/) (2022)
- [Calibrated and Partially Calibrated Semi-Generalized Homographies](https://mlsanity.com/paper/2103.06535/) (2021)
- [Camera Calibration using a Collimator System](https://mlsanity.com/paper/2409.20034/) (2024)
- [Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration](https://mlsanity.com/paper/2411.17240/) (2024)
- [Neural Lens Modeling](https://mlsanity.com/paper/2304.04848/) (2023)
- [Rotation Synchronization via Deep Matrix Factorization](https://mlsanity.com/paper/2305.05268/) (2023)
- [GeNIE: A Generalizable Navigation System for In-the-Wild Environments](https://mlsanity.com/paper/2506.17960/) (2025)
- [Refractive COLMAP: Refractive Structure-from-Motion Revisited](https://mlsanity.com/paper/2403.08640/) (2024)
- [Inferring bias and uncertainty in camera calibration](https://mlsanity.com/paper/2107.13484/) (2021)
- [Unbiased Estimator for Distorted Conics in Camera Calibration](https://mlsanity.com/paper/2403.04583/) (2024)
- [Deflectometry for specular surfaces: an overview](https://mlsanity.com/paper/2204.11592/) (2022)
- [Modeling dynamic target deformation in camera calibration](https://mlsanity.com/paper/2110.07322/) (2021)
- [Partially calibrated semi-generalized pose from hybrid point correspondences](https://mlsanity.com/paper/2209.15072/) (2022)
- [PuzzleBoard: A New Camera Calibration Pattern with Position Encoding](https://mlsanity.com/paper/2409.20127/) (2024)
- [High-precision visual navigation device calibration method based on collimator](https://mlsanity.com/paper/2502.18012/) (2025)
- [Collimator-assisted high-precision calibration method for event cameras](https://mlsanity.com/paper/2512.16092/) (2025)
- [Blazer: Laser Scanning Simulation using Physically Based Rendering](https://mlsanity.com/paper/2104.05430/) (2021)
- [TartanCalib: Iterative Wide-Angle Lens Calibration using Adaptive SubPixel Refinement of AprilTags](https://mlsanity.com/paper/2210.02511/) (2022)
- [Rotating-star Pattern for Camera Calibration](https://mlsanity.com/paper/2410.13371/) (2024)
- [Camera Calibration via Circular Patterns: A Comprehensive Framework with Detection Uncertainty and Unbiased Projection Model](https://mlsanity.com/paper/2506.16842/) (2025)
- [Neural Camera Models](https://mlsanity.com/paper/2208.12903/) (2022)
- [Revisiting Stereo Triangulation in UAV Distance Estimation](https://mlsanity.com/paper/2306.08939/) (2023)

## Abstract (arXiv)

Camera calibration is an essential first step in setting up 3D Computer Vision systems. Commonly used parametric camera models are limited to a few degrees of freedom and thus often do not optimally fit to complex real lens distortion. In contrast, generic camera models allow for very accurate calibration due to their flexibility. Despite this, they have seen little use in practice. In this paper, we argue that this should change. We propose a calibration pipeline for generic models that is fully automated, easy to use, and can act as a drop-in replacement for parametric calibration, with a focus on accuracy. We compare our results to parametric calibrations. Considering stereo depth estimation and camera pose estimation as examples, we show that the calibration error acts as a bias on the results. We thus argue that in contrast to current common practice, generic models should be preferred over parametric ones whenever possible. To facilitate this, we released our calibration pipeline at https://github.com/puzzlepaint/camera_calibration, making both easy-to-use and accurate camera calibration available to everyone.