# Fast and robust curve skeletonization for real-world elongated objects

## TL;DR

This paper presents a fast, robust, and automatic method for extracting curve skeletons from noisy 3D elongated objects, suitable for real-time applications like plant structure analysis.

## Contribution

The authors introduce a novel breadth-first search based approach that automatically detects junctions and spurious segments with minimal user adjustment, outperforming classical methods.

## Key findings

- Runs in hundreds of milliseconds to under four seconds on large datasets
- Automatically detects junction points and spurious segments
- Performs favorably compared to classical thinning algorithms

## Abstract

We consider the problem of extracting curve skeletons of three-dimensional, elongated objects given a noisy surface, which has applications in agricultural contexts such as extracting the branching structure of plants. We describe an efficient and robust method based on breadth-first search that can determine curve skeletons in these contexts. Our approach is capable of automatically detecting junction points as well as spurious segments and loops. All of that is accomplished with only one user-adjustable parameter. The run time of our method ranges from hundreds of milliseconds to less than four seconds on large, challenging datasets, which makes it appropriate for situations where real-time decision making is needed. Experiments on synthetic models as well as on data from real world objects, some of which were collected in challenging field conditions, show that our approach compares favorably to classical thinning algorithms as well as to recent contributions to the field.