# 3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras

arXiv: https://arxiv.org/abs/2506.00541 · HTML: https://mlsanity.com/paper/2506.00541/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 7 of 7)

- [Line-based 6-DoF Object Pose Estimation and Tracking With an Event Camera](https://mlsanity.com/paper/2408.03225/) (2024)
- [Stereo Event-based, 6-DOF Pose Tracking for Uncooperative Spacecraft](https://mlsanity.com/paper/2503.12732/) (2025)
- [A Universal Protocol to Benchmark Camera Calibration for Sports](https://mlsanity.com/paper/2404.09807/) (2024)
- [Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild](https://mlsanity.com/paper/2007.12806/) (2020)
- [On the Two-View Geometry of Unsynchronized Cameras](https://mlsanity.com/paper/1704.06843/) (2017)
- [Triangulation: Why Optimize?](https://mlsanity.com/paper/1907.11917/) (2019)
- [Camera Calibration using a Collimator System](https://mlsanity.com/paper/2409.20034/) (2024)

## Built on by (most cited first, 1 of 1)

- [An Opticalmechanics Framework for Dynamic Estimation of Multibody Systems](https://mlsanity.com/paper/2606.09383/) (2026)

## Abstract (arXiv)

Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points from asynchronous cameras is a significant challenge. It encompasses two coupled sub-problems: trajectory reconstruction and camera synchronization. Present methods typically address only one of these sub-problems individually. This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras, simultaneously solving both sub-problems. Firstly, we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization. Secondly, we develop models for camera temporal information and target motion, based on imaging mechanisms and target dynamics characteristics. The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters. Thirdly, we optimize the camera rotations alongside the camera time information and target motion parameters, using tighter and more continuous constraints on moving points. The reconstruction accuracy is significantly improved, especially when the camera rotations are inaccurate. Finally, the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method. The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation range of 15 ~ 20 km.