Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference

2026-04-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The paper "Topology-Aware Skeleton Detection via Lighthouse-Guided Structured Inference" by Daoyong Fu et al., submitted on April 22, 2026, and last revised on June 5, 2026, has been withdrawn. This work introduced Lighthouse-Skel, a topology-aware skeleton detection method via lighthouse-guided structured inference. Lighthouse-Skel aimed to address the challenge of discontinuous skeletons in natural images, which arise from slight pose variations. The method employed a dual-branch collaborative detection framework to jointly learn skeleton confidence fields and structural anchors, specifically endpoints and junction points. It also featured a lighthouse-guided topology completion strategy, utilizing detected junction points and breakpoints as "lighthouses" to reconnect segments along low-cost paths, enhancing continuity and structural integrity. The authors withdrew the submission due to identified "substantive issues" affecting result reliability, indicating a revision is in progress.

Key takeaway

For computer vision engineers evaluating new skeleton detection methods, be aware that the Lighthouse-Skel paper, arXiv:2604.20123, has been withdrawn due to identified substantive issues affecting result reliability. You should exercise caution when encountering references to this specific work in other literature. Await the revised and validated version before considering its proposed dual-branch framework or lighthouse-guided topology completion strategy for your projects.

Key insights

Lighthouse-Skel proposed a dual-branch, topology-aware skeleton detection method using structured inference and a lighthouse-guided completion strategy.

Principles

• Focus on structural continuity in skeleton detection.
• Jointly learn confidence fields and structural anchors.
• Use junction points as "lighthouses" for reconnection.

Method

Lighthouse-Skel uses a dual-branch framework to learn skeleton confidence and structural anchors (endpoints, junction points). A lighthouse-guided strategy then reconnects discontinuous segments along low-cost paths using these anchors.

Topics

• Skeleton Detection
• Computer Vision
• Topology-Aware Networks
• Structured Inference
• Image Analysis

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

Related on AIssential

• S23DR 2026 Winning Solution — A flow-matching DiT conditioned on scene tokens effectively reconstructs 3D wireframes from sparse multi-modal inputs.
• Geometric-Aware Hypergraph Reasoning for Novel Class Discovery in Point Cloud Segmentation — Hypergraph reasoning and geometric-aware prototypes improve novel class discovery in point cloud segmentation by capturing high-order and spatial relationships.
• Learned Non-Maximum Suppression for 3D Object Detection — Learned filtering modules, D2D-Rescore and GossipNet3D, enhance 3D object detection by replacing heuristic NMS with detection-to-detection relation leveraging.
• CAVE: A Structured Credit Assignment Approach for Fragmented Visual Evidence Reasoning — CAVE improves VLM reasoning by assigning structured, action-level credits for visual evidence exploration.
• PanoPlane: Plane-Aware Panoramic Completion for Sparse-View Indoor 3D Gaussian Splatting — Panoramic scene completion with layout-anchored attention steering significantly improves sparse-view 3D reconstruction.
• Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection — Structured latent projection in CBC-SLP preserves modality-specific information, enhancing multimodal semantic segmentation robustness.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.