Problem
Research questions and friction points this paper is trying to address.
deformable linear objects
video-to-model
automatic modeling
suture thread
motion reconstruction
Video-to-Model: Automatic Modeling of Deformable Linear Objects
🤖 AI Summary
This study addresses the challenge of manual parameter tuning in motion modeling for deformable linear objects by proposing the first automated "video-to-model" framework. The approach integrates structured numerical models with deep learning: a perception module tracks suture trajectories, while a spatiotemporal convolutional neural network automatically estimates parameters for the CBF-CLF-QP control model, enabling end-to-end construction of dynamic models directly from video data. Experimental results demonstrate that the framework achieves high-fidelity reconstruction of thread behavior under unseen configurations, exhibiting low tracking errors and accurately reproducing expected motions. By significantly reducing manual intervention, this work effectively enhances both the efficiency and generalization capability of physics-based modeling.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Video-to-Model
Deformable Linear Objects
Spatio-temporal CNN
CBF-CLF-QP
Automatic Parameter Estimation
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