# Self-Supervised Camera Self-Calibration from Video

arXiv: https://arxiv.org/abs/2112.03325 · HTML: https://mlsanity.com/paper/2112.03325/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 18 of 18)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [Unsupervised Learning of Depth and Ego-Motion from Video](https://mlsanity.com/paper/1704.07813/) (2017)
- [3D Packing for Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/1905.02693/) (2019)
- [Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue](https://mlsanity.com/paper/1603.04992/) (2016)
- [Learning to Recover 3D Scene Shape from a Single Image](https://mlsanity.com/paper/2012.09365/) (2020)
- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://mlsanity.com/paper/1904.04998/) (2019)
- [SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimation](https://mlsanity.com/paper/1810.01849/) (2018)
- [The Double Sphere Camera Model](https://mlsanity.com/paper/1807.08957/) (2018)
- [Visual-Inertial Mapping with Non-Linear Factor Recovery](https://mlsanity.com/paper/1904.06504/) (2019)
- [A Perceptual Measure for Deep Single Image Camera Calibration](https://mlsanity.com/paper/1712.01259/) (2017)
- [FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectification](https://mlsanity.com/paper/1804.04784/) (2018)
- [Horizon Lines in the Wild](https://mlsanity.com/paper/1604.02129/) (2016)
- [Progressively Complementary Network for Fisheye Image Rectification Using Appearance Flow](https://mlsanity.com/paper/2103.16026/) (2021)
- [Single View Metrology in the Wild](https://mlsanity.com/paper/2007.09529/) (2020)
- [Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion](https://mlsanity.com/paper/2008.06630/) (2020)
- [Learning Perspective Undistortion of Portraits](https://mlsanity.com/paper/1905.07515/) (2019)
- [A Deep Ordinal Distortion Estimation Approach for Distortion Rectification](https://mlsanity.com/paper/2007.10689/) (2020)

## Built on by (most cited first, 17 of 17)

- [MUSt3R: Multi-view Network for Stereo 3D Reconstruction](https://mlsanity.com/paper/2503.01661/) (2025)
- [Towards Zero-Shot Scale-Aware Monocular Depth Estimation](https://mlsanity.com/paper/2306.17253/) (2023)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [Zero-Shot Novel View and Depth Synthesis with Multi-View Geometric Diffusion](https://mlsanity.com/paper/2501.18804/) (2025)
- [Self-Calibrating Gaussian Splatting for Large Field of View Reconstruction](https://mlsanity.com/paper/2502.09563/) (2025)
- [DeLiRa: Self-Supervised Depth, Light, and Radiance Fields](https://mlsanity.com/paper/2304.02797/) (2023)
- [Physics and semantic informed multi-sensor calibration via optimization theory and self-supervised learning](https://mlsanity.com/paper/2206.02856/) (2022)
- [Robust Self-Supervised Extrinsic Self-Calibration](https://mlsanity.com/paper/2308.02153/) (2023)
- [LiFCal: Online Light Field Camera Calibration via Bundle Adjustment](https://mlsanity.com/paper/2408.11682/) (2024)
- [CalibAnyView: Beyond Single-View Camera Calibration in the Wild](https://mlsanity.com/paper/2605.14615/) (2026)
- [Self-Supervised Online Camera Calibration for Automated Driving and Parking Applications](https://mlsanity.com/paper/2308.08495/) (2023)
- [Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry](https://mlsanity.com/paper/2406.00929/) (2024)
- [PriorFormer: A Transformer for Real-time Monocular 3D Human Pose Estimation with Versatile Geometric Priors](https://mlsanity.com/paper/2508.18238/) (2025)
- [Neural Camera Models](https://mlsanity.com/paper/2208.12903/) (2022)
- [NOCaL: Calibration-Free Semi-Supervised Learning of Odometry and Camera Intrinsics](https://mlsanity.com/paper/2210.07435/) (2022)
- [Tensor-Based Self-Calibration of Cameras via the TrifocalCalib Method](https://mlsanity.com/paper/2509.17620/) (2025)
- [Rolling Shutter Camera Self-Calibration](https://mlsanity.com/paper/2608.01509/) (2026)

## Abstract (arXiv)

Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams. In practice, calibration is a laborious procedure requiring specialized data collection and careful tuning. This process must be repeated whenever the parameters of the camera change, which can be a frequent occurrence for mobile robots and autonomous vehicles. In contrast, self-supervised depth and ego-motion estimation approaches can bypass explicit calibration by inferring per-frame projection models that optimize a view synthesis objective. In this paper, we extend this approach to explicitly calibrate a wide range of cameras from raw videos in the wild. We propose a learning algorithm to regress per-sequence calibration parameters using an efficient family of general camera models. Our procedure achieves self-calibration results with sub-pixel reprojection error, outperforming other learning-based methods. We validate our approach on a wide variety of camera geometries, including perspective, fisheye, and catadioptric. Finally, we show that our approach leads to improvements in the downstream task of depth estimation, achieving state-of-the-art results on the EuRoC dataset with greater computational efficiency than contemporary methods.