# Automated Feature Tracking for Real-Time Kinematic Analysis and Shape Estimation of Carbon Nanotube Growth

arXiv: https://arxiv.org/abs/2508.19232 · HTML: https://mlsanity.com/paper/2508.19232/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [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)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [CoTracker: It is Better to Track Together](https://mlsanity.com/paper/2307.07635/) (2023)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement](https://mlsanity.com/paper/2306.08637/) (2023)
- [Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories](https://mlsanity.com/paper/2204.04153/) (2022)
- [ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation](https://mlsanity.com/paper/2304.03608/) (2023)
- [Predicting mechanical properties of Carbon Nanotube (CNT) images Using Multi-Layer Synthetic Finite Element Model Simulations](https://mlsanity.com/paper/2307.07912/) (2023)
- [Stabilizing unstable periodic orbit of unknown fractional-order systems via adaptive delayed feedback control](https://mlsanity.com/paper/2208.06783/) (2022)

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

- [Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description](https://mlsanity.com/paper/2512.11098/) (2025)

## Abstract (arXiv)

Carbon nanotubes (CNTs) are critical building blocks in nanotechnology, yet the characterization of their dynamic growth is limited by the experimental challenges in nanoscale motion measurement using scanning electron microscopy (SEM) imaging. Existing ex situ methods offer only static analysis, while in situ techniques often require manual initialization and lack continuous per-particle trajectory decomposition. We present Visual Feature Tracking (VFTrack) an in-situ real-time particle tracking framework that automatically detects and tracks individual CNT particles in SEM image sequences. VFTrack integrates handcrafted or deep feature detectors and matchers within a particle tracking framework to enable kinematic analysis of CNT micropillar growth. A systematic using 13,540 manually annotated trajectories identifies the ALIKED detector with LightGlue matcher as an optimal combination (F1-score of 0.78, α-score of 0.89). VFTrack motion vectors decomposed into axial growth, lateral drift, and oscillations, facilitate the calculation of heterogeneous regional growth rates and the reconstruction of evolving CNT pillar morphologies. This work enables advancement in automated nano-material characterization, bridging the gap between physics-based models and experimental observation to enable real-time optimization of CNT synthesis.