# Reconstruction of 3D flight trajectories from ad-hoc camera networks

arXiv: https://arxiv.org/abs/2003.04784 · HTML: https://mlsanity.com/paper/2003.04784/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 12 of 12)

- [Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?](https://mlsanity.com/paper/2312.04548/) (2023)
- [Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID](https://mlsanity.com/paper/2503.17237/) (2025)
- [Vision-Based Anti Unmanned Aerial Technology: Opportunities and Challenges](https://mlsanity.com/paper/2507.10006/) (2025)
- [LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges](https://mlsanity.com/paper/2508.03331/) (2025)
- [3D Trajectory Reconstruction of Drones using a Single Camera](https://mlsanity.com/paper/2309.02801/) (2023)
- [UAVDB: Point-Guided Masks for UAV Detection and Segmentation](https://mlsanity.com/paper/2409.06490/) (2024)
- [Visual Sync: Multi-Camera Synchronization via Cross-View Object Motion](https://mlsanity.com/paper/2512.02017/) (2025)
- [LRDDv3: High-Resolution Long-Range Drone Detection Dataset with Range Information and Thermal Data](https://mlsanity.com/paper/2605.25942/) (2026)
- [3D Reconstruction from public webcams](https://mlsanity.com/paper/2108.09476/) (2021)
- [A Modular Pipeline for 3D Object Tracking Using RGB Cameras](https://mlsanity.com/paper/2503.04322/) (2025)
- [DroneKey++: A Size Prior-free Method and New Benchmark for Drone 3D Pose Estimation from Sequential Images](https://mlsanity.com/paper/2602.06211/) (2026)
- [DroneDAR: Long-Range Drone Distance Estimation Using Monocular Vision and Bounding-Box Features](https://mlsanity.com/paper/2606.07756/) (2026)

## Abstract (arXiv)

We present a method to reconstruct the 3D trajectory of an airborne robotic system only from videos recorded with cameras that are unsynchronized, may feature rolling shutter distortion, and whose viewpoints are unknown. Our approach enables robust and accurate outside-in tracking of dynamically flying targets, with cheap and easy-to-deploy equipment. We show that, in spite of the weakly constrained setting, recent developments in computer vision make it possible to reconstruct trajectories in 3D from unsynchronized, uncalibrated networks of consumer cameras, and validate the proposed method in a realistic field experiment. We make our code available along with the data, including cm-accurate ground-truth from differential GNSS navigation.