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

• URL: http://arxiv.org/abs/2608.10648v1
• Date: Tue, 11 Aug 2026 08:32:28 GMT
• Title: Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks
• Abstract summary: We propose a novel segmentation training architecture tailored for top-layer fabric segmentation in stacked fabrics.<n>The method extends the classical encoder-decoder framework by introducing two specialized branches.<n> Experiments on a real-world fabric dataset demonstrate that the training approach outperforms established baselines.
• Score: 8.266820805797826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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