WireSeg-32K：用于导线实例分割的基于物理的合成数据集
WireSeg-32K: A Physics-Grounded Synthetic Dataset for Wire Instance Segmentation
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中文总结 AI 辅助
针对导线等可变形线性物体分割难、真实场景标注成本高的问题，提出含32000张图像的WireSeg-32K合成数据集，开发DeformX协同仿真流水线生成数据，经LoRA微调SAM3后真实场景mAP@75提升10.2%，验证了该合成数据的迁移价值。
中文摘要 AI 辅助
导线和电缆等可变形线性物体难以分割，因为它们较细、高度可变形且经常自遮挡，而在真实场景中获取大规模实例级标注的成本很高。现有资源要么聚焦于电缆追踪或受限场景下的语义分割，要么生成视觉上逼真但无物理依据的导线变形图像。我们提出WireSeg-32K，这是一个用于导线实例分割的合成数据集，包含32000张RGB图像、实例掩码、深度图，以及带有标注的互补真实世界测试集。为生成该数据集，我们开发了DeformX，这是一种耦合Cosserat杆动力学与超写实Isaac Sim渲染的协同仿真流水线，可生成符合物理规律、接触一致的导线形状、基于CAD的导线资产以及多样的视觉合理场景。作为一个简单基线，仅在WireSeg-32K上进行LoRA微调SAM3，就比现成模型将真实世界mAP@75提升了10.2%，表明基于物理的合成数据可迁移至真实导线感知任务。
英文摘要
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.
发表机构
- Harvard University（哈佛大学）
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
