TRACE：杂乱环境中单色电缆的交互式双向追踪
TRACE: Interactive Bi-Directional Tracing of Monochrome Cables Amid Clutter
浏览论文内容
中文总结 AI 辅助
TRACE结合双向追踪与交互式感知原语，在杂乱环境中将单色电缆追踪准确率从约60%提升至约90%，优于现有方法。
中文摘要 AI 辅助
对电缆等可变形线性物体（DLOs）的精确状态估计（追踪）是数据中心、制造业、建筑业、家庭和手术中的一个关键挑战，在这些场景中，精确的电缆管理直接影响操作安全和效率。然而，在前景和背景杂乱的环境中，解析多根单色电缆的状态因遮挡、重叠和模糊交叉而面临挑战。我们提出了双向路由与电缆估计（TRACE），该方法将双向电缆追踪与交互式感知原语——发散推挤和簇扩张——相结合，以主动解决歧义。基于110次物理实验的评估表明，在复杂场景（最多4根电缆和40个交叉点）中，TRACE能将正确追踪的电缆长度百分比从最强先前方法HANDLOOM 2.0的约60%提升至约90%，并且也优于RT-DLO、Nano Banana Pro和ChatGPT 5.2。在配备NVIDIA GeForce RTX 4090 GPU的工作站上进行的一次试运行中，平均每根电缆的计算时间为0.4秒。项目网站：此https URL。
英文摘要
Accurate state estimation (tracing) of Deformable Linear Objects (DLOs) such as cables is a critical challenge for data centers, manufacturing, construction, homes, and surgery, where precise cable management directly impacts operational safety and efficiency. However, resolving the state of multiple monochrome cables amid foreground and background clutter poses challenges due to occlusions, overlap, and ambiguous crossings. We present Two-way Routing And Cable Estimation (TRACE), which combines bi-directional cable tracing with interactive perception primitives-Divergence Push and Cluster Dilation-to actively resolve ambiguities. Evaluation with 110 physical experiments suggests that TRACE can increase the percentage of cable length correctly traced in complex scenarios (with up to 4 cables and 40 crossings) from ~60% with the strongest prior method, HANDLOOM 2.0, to ~90%, outperforming RT-DLO, Nano Banana Pro, and ChatGPT 5.2 as well. For a trial run on a workstation with an NVIDIA GeForce RTX 4090 GPU, the average computation time is 0.4 seconds per cable. Project website: https://trace-paper.github.io/.
发表机构
- AUTOLab at the University of California, Berkeley（加州大学伯克利分校AUTOLab）
机构由 AI 辅助整理，请以论文原文为准。
