# Wide-Baseline Multi-Camera Calibration using Person Re-Identification

arXiv: https://arxiv.org/abs/2104.08568 · HTML: https://mlsanity.com/paper/2104.08568/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 64 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [Geometric Loss Functions for Camera Pose Regression with Deep Learning](https://mlsanity.com/paper/1704.00390/) (2017)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Geometry-Aware Learning of Maps for Camera Localization](https://mlsanity.com/paper/1712.03342/) (2017)
- [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [Improving Person Re-identification by Attribute and Identity Learning](https://mlsanity.com/paper/1703.07220/) (2017)
- [Part-Aligned Bilinear Representations for Person Re-identification](https://mlsanity.com/paper/1804.07094/) (2018)
- [Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network](https://mlsanity.com/paper/1707.09733/) (2017)
- [Camera Style Adaptation for Person Re-identification](https://mlsanity.com/paper/1711.10295/) (2017)
- [Image-based Localization using Hourglass Networks](https://mlsanity.com/paper/1703.07971/) (2017)
- [Multi-Level Factorisation Net for Person Re-Identification](https://mlsanity.com/paper/1803.09132/) (2018)
- [Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification](https://mlsanity.com/paper/1803.09882/) (2018)
- [Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification](https://mlsanity.com/paper/1803.09937/) (2018)
- [Person Re-identification with Deep Similarity-Guided Graph Neural Network](https://mlsanity.com/paper/1807.09975/) (2018)
- [Relative Camera Pose Estimation Using Convolutional Neural Networks](https://mlsanity.com/paper/1702.01381/) (2017)
- [Introduction to Camera Pose Estimation with Deep Learning](https://mlsanity.com/paper/1907.05272/) (2019)
- [WxBS: Wide Baseline Stereo Generalizations](https://mlsanity.com/paper/1504.06603/) (2015)
- [UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision](https://mlsanity.com/paper/2001.07252/) (2020)
- [Deep Group-shuffling Random Walk for Person Re-identification](https://mlsanity.com/paper/1807.11178/) (2018)
- [ELF: Embedded Localisation of Features in pre-trained CNN](https://mlsanity.com/paper/1907.03261/) (2019)

## Built on by (most cited first, 15 of 15)

- [Reconstructing People, Places, and Cameras](https://mlsanity.com/paper/2412.17806/) (2024)
- [Virtual Correspondence: Humans as a Cue for Extreme-View Geometry](https://mlsanity.com/paper/2206.08365/) (2022)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras](https://mlsanity.com/paper/2401.15616/) (2024)
- [Humans as a Calibration Pattern: Dynamic 3D Scene Reconstruction from Unsynchronized and Uncalibrated Videos](https://mlsanity.com/paper/2412.19089/) (2024)
- [Kineo: Calibration-Free Metric Motion Capture From Sparse RGB Cameras](https://mlsanity.com/paper/2510.24464/) (2025)
- [Spatiotemporal Multi-Camera Calibration using Freely Moving People](https://mlsanity.com/paper/2502.12546/) (2025)
- [Multi-View Person Matching and 3D Pose Estimation with Arbitrary Uncalibrated Camera Networks](https://mlsanity.com/paper/2312.01561/) (2023)
- [LiFMCR: Dataset and Benchmark for Light Field Multi-Camera Registration](https://mlsanity.com/paper/2510.13729/) (2025)
- [YOWO: You Only Walk Once to Jointly Map An Indoor Scene and Register Ceiling-mounted Cameras](https://mlsanity.com/paper/2511.16521/) (2025)
- [CasCalib: Cascaded Calibration for Motion Capture from Sparse Unsynchronized Cameras](https://mlsanity.com/paper/2405.06845/) (2024)
- [SteerPose: Simultaneous Extrinsic Camera Calibration and Matching from Articulation](https://mlsanity.com/paper/2506.01691/) (2025)
- [RPGD: RANSAC-P3P Gradient Descent for Extrinsic Calibration in 3D Human Pose Estimation](https://mlsanity.com/paper/2602.13901/) (2026)
- [Multi-Camera Self-Calibration in Sports Motion Capture: Leveraging Human and Stick Poses](https://mlsanity.com/paper/2604.17567/) (2026)
- [Syn2RealTrack: Bridging the Gap Between Synthetic and Real-World Datasets for Online Multi-View Multi-Target Tracking](https://mlsanity.com/paper/2608.24130/) (2026)

## Abstract (arXiv)

We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and public spaces. This task is challenging since detecting and matching the same 3D keypoint observed from two very different camera views is difficult, making standard structure-from-motion (SfM) pipelines inapplicable. In such circumstances, treating people in the scene as "keypoints" and associating them across different camera views can be an alternative method for obtaining correspondences. Based on this intuition, we propose a method that uses ideas from person re-identification (re-ID) for wide-baseline camera calibration. Our method first employs a re-ID method to associate human bounding boxes across cameras, then converts bounding box correspondences to point correspondences, and finally solves for camera pose using multi-view geometry and bundle adjustment. Since our method does not require specialized calibration targets except for visible people, it applies to situations where frequent calibration updates are required. We perform extensive experiments on datasets captured from scenes of different sizes, camera settings (indoor and outdoor), and human activities (walking, playing basketball, construction). Experiment results show that our method achieves similar performance to standard SfM methods relying on manually labeled point correspondences.