视频到模型：可变形线性物体的自动建模
Video-to-Model: Automatic Modeling of Deformable Linear Objects
- University of Maryland（马里兰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出视频到模型框架，利用CBF--CLF--QP模型和时空CNN，从视频自动估计参数并模拟缝合线运动，减少手动调参，实验验证低跟踪误差。
AI中文摘要：
本文提出了一种视频到模型框架，用于从输入视频自动建模可变形缝合线的运动。我们利用最近开发的CBF--CLF--QP数值模型，该模型通过选择少量参数简化了可变形细线运动的表征。感知模块首先在视频中定位并跟踪缝合线，生成有序的缝合线节点序列。随后，观察到的缝合线运动由时空CNN网络处理，该网络估计结构化CBF--CLF--QP模型的有效参数。这些参数用于在用户定义的针速度输入下模拟缝合线。使用未见过的缝合线配置和运动进行的实验表明，该框架能够可靠地从视频中重建缝合线行为，自动配置结构化模型，并以低跟踪误差重现预期的缝合线运动。所提出的方法减少了手动参数调整的需求，并为基于视频的可变形线性物体自动建模迈出了一步。
英文摘要：
This paper presents a video-to-model framework for automatically modeling the motion of a deformable suture thread from an input video. We utilize a recently developed CBF--CLF--QP numerical model that simplifies the characterization of deformable string motion through the selection of a small number of parameters. A perception module first localizes and tracks the thread in video, producing an ordered sequence of thread nodes. The observed thread motion is then processed by a spatio-temporal CNN network that estimates the effective parameters of a structured CBF--CLF--QP model. These parameters are used to simulate the thread under a user-defined needle velocity input. Experiments using unseen thread configurations and motion demonstrate that the framework can reliably reconstruct the thread behavior from video, automatically configure the structured model, and reproduce the expected thread motion with low tracking error. The proposed approach reduces the need for manual parameter tuning and provides a step toward automatic video-based modeling of deformable linear objects.
