针对叠放布料顶层分割难题，提出双分支感知网络提升边界与形状精度。
Precise Top-Layer Fabric Segmentation for Fabric Destacking with Edge- and Shape-Aware Deep Networks
- 引入边缘与形状双分支监督，优化编码器-解码器结构
- 在真实数据集上分割精度显著超越基线方法
- 适合机器人抓取、服装制造等需要精细布料识别的场景
布料去堆叠需精准分割最上层布料，但因布料边界细微且各层视觉相似度高，传统语义与边缘分割方法常失效，制约机器人操作性能。本文提出一种专为叠放布料顶层分割设计的新训练架构，扩展经典编码器-解码器框架，引入边缘感知分支和形状感知分支，分别增强边界刻画能力，并通过计算机辅助设计（CAD）模型生成的参考掩码引导网络捕捉并对齐整体布料形状。在真实世界布料数据集上的实验表明，该方法在定量指标与消融研究中均优于现有基线，验证了多分支设计的有效性。
原文摘要 · Abstract (English)
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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