用物理仿真生成32000张线缆实例分割数据，提升真实场景识别效果。
WireSeg-32K: A Physics-Grounded Synthetic Dataset for Wire Instance Segmentation
- 结合杆状动力学与渲染，生成符合物理规律的线缆形态。
- 仅用合成数据微调SAM3，真实场景mAP@75提升10.2%。
- 适合做线缆分割、机器人抓取或需要物理真实性的视觉研究者。
细长可变形物体如电线和电缆因纤细、易形变且频繁自遮挡，难以分割；而真实场景中大规模实例级标注成本高昂。现有资源或聚焦电缆追踪、或在受限条件下进行语义分割，或生成视觉逼真但缺乏物理真实的图像。我们提出WireSeg-32k，一个用于线缆实例分割的合成数据集，包含32,000张RGB图像、实例掩码、深度图，并附带一个带标注的真实测试集。为生成该数据集，我们开发了DeformX，一种耦合Cosserat杆动力学与光栅化渲染的联合仿真流程，实现物理合理的接触一致线缆形状、基于CAD的线缆资产及多样化的视觉合理场景。作为基线，仅用WireSeg-32k对SAM3进行LoRA微调，即可使真实场景下的mAP@75提升10.2%，表明物理基础的合成数据可有效迁移至真实线缆感知。
原文摘要 · Abstract (English)
Deformable linear objects such as wires and cables are difficult to segment because they are thin, highly deformable, and frequently self-occluded, while large-scale instance-level annotations are expensive to obtain in real scenes. Existing resources either focus on cable tracing or semantic segmentation under constrained settings, or generate visually plausible images without physically grounded wire deformation. We present WireSeg-32k, a synthetic dataset for wire instance segmentation with 32,000 RGB images, instance masks, depth maps, and a complementary real-world test set with annotations. To generate this dataset, we develop DeformX, a co-simulation pipeline that couples Cosserat-rod dynamics with photorealistic Isaac Sim rendering, enabling physically plausible, contact-consistent wire shapes, CAD-based wire assets, and diverse visually grounded scenes. As a simple baseline, LoRA fine-tuning SAM3 on WireSeg-32k alone improves real-world mAP@75 by 10.2% over the off-the-shelf model, showing that physically grounded synthetic data can transfer to real wire perception.
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