mlsanityData to 14 Oct 2026

Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks

arXiv 2608.10648 ↗First posted 11 Aug 2026cs.CV cs.ROcited by 0

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Abstract (arXiv)

Fabric destacking requires precise segmentation of the topmost fabric layer, a task complicated by subtle fabric boundaries and high visual similarity between fabric layers. Existing semantic and edge-based segmentation approaches often struggle with these complexities, limiting the performance of robotic manipulation for different tasks. In this work, a novel segmentation training architecture tailored for top-layer fabric segmentation in stacked fabrics is proposed. The method extends the classical encoder-decoder framework by introducing two specialized branches - an edge-aware branch and a shape-aware branch - that are used to supervise the backbone network for better tuning. The edge-aware branch enhances boundary delineation, while the shape-aware branch guides the network to capture and align the overall fabric shape with reference masks derived from Computer Aided Design (CAD) models. Experiments on a real-world fabric dataset demonstrate that the training approach outperforms established baselines, verifying the effectiveness of the multi-branch design through both quantitative results and ablation studies.

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References · 15

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  12. 2016DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentationcited by 84
3 more
  1. 2017SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentationcited by 49
  2. 2019ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentationcited by 38
  3. 2017CASENet: Deep Category-Aware Semantic Edge Detectioncited by 37

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