mlsanityData to 7 Oct 2026

Topology-Preserving Polygon Augmentation for Segmentation in Structured Visual Domains

arXiv 2603.14764 ↗First posted 16 Mar 2026cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Geometric data augmentation is widely used in segmentation workflows, but polygon annotations are often assumed to remain valid after transformation. This assumption can fail in structured domains such as architectural floorplan analysis, where a region may contain an interior void encoded as part of a single ordered polygon chain. Cropping or clipping can remove bridge vertices in this chain, causing one semantic region to split into disconnected components. We propose a lightweight topology-preserving augmentation strategy that repairs missing adjacency relations in index space while preserving the original vertex order. The method adds minimal overhead and can be integrated into existing preprocessing workflows. Experiments show that the proposed approach achieves near-perfect Cyclic Adjacency Preservation (CAP) across common geometric transformations and improves annotation consistency in polygon-based segmentation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 16 Mar 2026.

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18 of 29 references.

References · 18

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  12. 2019Large-scale interactive object segmentation with human annotatorscited by 109
6 more
  1. 2019PolyTransform: Deep Polygon Transformer for Instance Segmentationcited by 92
  2. 2019CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysiscited by 49
  3. 2018FloorNet: A Unified Framework for Floorplan Reconstruction from 3D Scanscited by 35
  4. 2021Scale-aware Automatic Augmentation for Object Detectioncited by 23
  5. 2017Augmentor: An Image Augmentation Library for Machine Learningcited by 14
  6. 2024AugmenTory: A Fast and Flexible Polygon Augmentation Librarycited by 1

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