SOURCE-LINKED INTELLIGENCE

WireSeg-32K: A Physics-Grounded Synthetic Dataset for Wire Instance Segmentation

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

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 annotatio

Read original source ↗ Open in workspace

recordType

paper

region

Global