# Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference

arXiv: https://arxiv.org/abs/2604.20123 · HTML: https://mlsanity.com/paper/2604.20123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Richer Convolutional Features for Edge Detection](https://mlsanity.com/paper/1612.02103/) (2016)
- [DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images](https://mlsanity.com/paper/1609.03659/) (2016)
- [SRN: Side-output Residual Network for Object Symmetry Detection in the Wild](https://mlsanity.com/paper/1703.02243/) (2017)
- [DeepFlux for Skeletons in the Wild](https://mlsanity.com/paper/1811.12608/) (2018)
- [Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs](https://mlsanity.com/paper/1603.09446/) (2016)
- [Minimal Paths for Tubular Structure Segmentation with Coherence Penalty and Adaptive Anisotropy](https://mlsanity.com/paper/1809.07987/) (2018)
- [Adaptive Linear Span Network for Object Skeleton Detection](https://mlsanity.com/paper/2011.03972/) (2020)
- [Linear Span Network for Object Skeleton Detection](https://mlsanity.com/paper/1807.09601/) (2018)
- [Hi-Fi: Hierarchical Feature Integration for Skeleton Detection](https://mlsanity.com/paper/1801.01849/) (2018)
- [Progressive Minimal Path Method with Embedded CNN](https://mlsanity.com/paper/2204.00944/) (2022)
- [Grouping Boundary Proposals for Fast Interactive Image Segmentation](https://mlsanity.com/paper/2309.04169/) (2023)

## Abstract (arXiv)

In natural images, object skeletons are used to represent geometric shapes. However, even slight variations in pose or movement can cause noticeable changes in skeleton structure, increasing the difficulty of detecting the skeleton and often resulting in discontinuous skeletons. Existing methods primarily focus on point-level skeleton point detection and overlook the importance of structural continuity in recovering complete skeletons. To address this issue, we propose Lighthouse-Skel, a topology-aware skeleton detection method via lighthouse-guided structured inference. Specifically, we introduce a dual-branch collaborative detection framework that jointly learns skeleton confidence field and structural anchors, including endpoints and junction points. The spatial distributions learned by the point branch guide the network to focus on topologically vulnerable regions, which improves the accuracy of skeleton detection. Based on the learned skeleton confidence field, we further propose a lighthouse-guided topology completion strategy, which uses detected junction points and breakpoints as lighthouses to reconnect discontinuous skeleton segments along low-cost paths, thereby improving skeleton continuity and structural integrity. Experimental results on four public datasets demonstrate that the proposed method achieves competitive detection accuracy while substantially improving skeleton connectivity and structural integrity.