Source-linked AI summary
Video-to-Model: Automatic Modeling of Deformable Linear Objects
Akshun Sharma, Kimia Forghani, Yancy Diaz-Mercado
TL;DR
Accurately predicting suture-thread response to needle motion is challenging because thread behavior varies with geometry and material properties. The paper uses video-based thread tracking and a spatio-temporal CNN to estimate parameters for a CBF–CLF–QP model. The configured model reproduced a held-out thread trajectory with low error without manual parameter tuning.
Problem
Suture-thread response to needle motion is difficult to model because geometry and material properties affect behavior, while conventional force-based models can be costly and sensitive to uncertain properties.
Source: §1 · p. 1Method
The framework tracks ordered thread nodes in video and uses their observed motion with needle motion to estimate effective CBF–CLF–QP parameters for simulation.
Source: §1 · p. 2, §3 · p. 4, §3.2 · p. 5Results
0.194 mm mean node error was reported for the simulated silk thread replay of an asymmetric flower-petal needle trajectory, and the configured model reproduced a held-out trajectory with very low error.
Source: §5.0.4 · p. 13, §6 · p. 13Takeaways & Limitations
The configured model can reproduce observed thread behavior on a held-out trajectory without manual parameter tuning.
Source: §6 · p. 13Segmentation and tracing errors can impair parameter predictions, and some excitation trajectories do not distinguish individual parameters sufficiently for unique identification.
Source: §6 · p. 13
Abstract
from arXiv · showhide
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.
1 Introduction
Suture threads are difficult to model because their flexible motion depends on geometry, material properties, and environmental interaction. This paper estimates CBF–CLF–QP parameters from video to configure a thread-specific model automatically.
1 Introduction
- Suture motion varies with geometry, material properties, and environmental interaction, making accurate modeling challenging.Bending, stretching, and stiffness can produce different responses to the same input.Source: §1 · p. 1
- Physics-based thread models represent masses, elastic and damping forces, and contact, but added modeled elements raise computational cost and uncertain properties can reduce prediction accuracy.The CBF–CLF–QP formulation instead constrains ordered-node velocities to capture connectivity and stiffness without explicitly calculating all mechanical forces.Source: §1 · p. 1
- The video-to-model framework localizes the thread as ordered nodes, then uses a spatio-temporal CNN to estimate parameters for the CBF–CLF–QP model.The estimated parameters configure the model to reproduce observed thread motion.Source: §1 · p. 2
2 Background
The background presents a discrete-node model and a CBF–CLF–QP formulation for constraining thread motion. Connectivity constraints limit node separation, while stiffness and motion objectives shape the simulated response.
2 Background
- The CBF–CLF–QP formulation introduced in models the suture by constraining discrete-node velocities rather than explicitly computing mechanical forces.The formulation supplies the model whose parameters this paper later estimates from observations.Source: §2 · p. 2
2.1 Discrete Suture-Thread Model
- Each thread node is modeled as a single integrator, and the QP determines velocities under geometric constraints before node positions are updated by forward Euler.The needle follows user-provided velocity, and its lead node is x_0.Source: §2.1 · pp. 2–3
2.2 Primer on CBFs and CLFs
- The connectivity CBF encodes a safe set in which adjacent nodes remain within the maximum allowable separation; γ controls the response near that boundary.The paper characterizes γ as controlling thread stretchiness.Source: §2.2 · p. 3
- The CLF drives the thread toward a preferred rest configuration to model stiffness, while the QP follows desired motion subject to connectivity and stiffness constraints.The QP can relax the CLF constraint when needed to preserve feasibility.Source: §2.2 · pp. 3–4
- A velocity-retention factor κ models residual drift when the needle stops; larger κ yields more persistent drift, while smaller κ makes motion decay faster.Different γ, α, and κ choices produce different simulated thread behaviors.Source: §2.2 · pp. 3–4
3 Problem Statement
The problem is to recover a thread’s changing configuration from monocular video and identify effective CBF–CLF–QP parameters so simulation matches the observed motion. The section describes representing the thread as ordered nodes and learning parameters from observed thread and needle motion.
3 Problem Statement
- The task is to estimate a thread’s configuration over time from video and identify effective CBF–CLF–QP parameters that make simulated motion match observed behavior.Source: §3 · p. 4
3.1 Thread State Observation from Video
- At each timestamp, the thread is represented as a planar curve and discretized into ordered nodes running from the needle-attached endpoint to the opposite endpoint.The needle attachment point is estimated separately, and the resulting trajectory is used for parameter identification.Source: §3.1 · p. 4
- The recorded image is modeled through an unknown observation operator, visual effects, and measurement noise, while the thread curve is estimated from the video.Source: §3.1 · p. 4
3.2 Video-Based Dynamical Parameter Identification
- The effective parameters govern connectivity response, stiffness response, and velocity retention, and are inferred from motion while assumed constant within each video.A learned mapping predicts the parameters, which instantiate the model for simulation under observed needle motion; simulated and observed trajectories are compared using mean node tracking error.Source: §3.2 · p. 5
- Simulation starts from the first observed state and uses observed needle motion to generate node positions for comparison with the observed trajectory.The comparison uses mean node tracking error.Source: §3.2 · p. 5
4.1 Thread Localization and Tracking Module
The localization and tracking module converts video frames into an ordered thread centerline. It combines U-Net segmentation with mask cleanup and temporally informed curve tracing.
4.1.1 U-Net Segmentation
- A four-level U-Net produces a thread-probability map from each RGB frame, which is thresholded and cleaned before skeletonization, pruning, and tracing into an ordered centerline.The segmentation dataset uses rendered RGB frames and binary masks, with randomized background appearance, illumination, thread properties, noise, and needle appearance.Source: §4.1.1 · pp. 5–6, Figure 2 · p. 6
4.1.2 Mask Post-Processing
- Probability maps are blended across consecutive frames, then morphological closing, small-component removal, skeletonization, and branch pruning clean the binary mask.The closing operation uses a 3 × 3 kernel, and components smaller than 12 pixels are removed.Source: §4.1.2 · pp. 6–7
4.1.3 Curve Tracking
- Curve tracing uses local direction and the preceding frame’s thread direction at self-crossings, while the needle-nearest endpoint fixes the curve’s ordering.The curve is sampled at approximately equal arc-length intervals into 30 nodes; missing detections are interpolated and tracking noise is filtered.Source: §4.1.3 · p. 7
4.2 Spatio-Temporal CNN Module
The CNN input is built by temporally aligning tracked thread and needle motion, then encoding each node and frame with spatial and motion features. Each node-frame vector contains twelve features.
4.2.1 Temporal Alignment and Velocity Estimation
- Frames are sampled every 0.048 s, and thread-node and needle velocities are estimated from position changes between consecutive frames.Source: §4.2.1 · p. 7
4.2.2 Spatial–Temporal Feature Construction
- The spatial features include distance from the needle or preceding node and local bending represented by changes in direction between adjacent nodes.Source: §4.2.2 · pp. 7–8
- Each node-frame vector contains twelve features, including relative position and velocity, inter-node distance, local curvature, connectivity, thread position, needle velocity, speed context, and stiffness.Thread and needle velocity components are normalized using a trajectory-specific speed scale, with a speed-context feature retaining information about absolute motion speed.Source: §4.2.2 · pp. 7–8
4.2.3 Spatial Node Encoder
The spatial node encoder maps each node’s features into a representation and processes ordered neighboring nodes with residual convolutions. Spatial attention and raw-feature statistics are then combined into a frame embedding.
4.2.3 Spatial Node Encoder
- Each node’s feature vector is mapped to a 64-dimensional representation, and three residual one-dimensional convolutional blocks gather information along the ordered thread nodes.The convolutions use kernel size seven.Source: §4.2.3 · p. 8
4.2.4 Spatial Attention and Frame Representation
- Spatial attention assigns weights to nodes and pools their representations into a frame-level attended representation.The attention weights sum to one across nodes.Source: §4.2.4 · p. 8
- The attended representation is fused with per-feature means, standard deviations, minima, and maxima across nodes to form a 64-dimensional frame embedding.These raw-feature statistics retain information about absolute feature magnitudes after spatial normalization.Source: §4.2.4 · pp. 8–9
4.2.5 Temporal Encoding and Attention
The network encodes thread shape and motion history with recurrent temporal representations, attention, and sequence-level statistics, then predicts scaled CBF–CLF–QP parameters. Training uses virtual materials and different needle motions, with supervised parameter and cross-motion consistency losses.
4.2.5 Temporal Encoding and Attention
- Four attention heads focus on different motion phases, while global temporal statistics join attended representations in the sequence-level embedding.A two-layer GRU incorporates current shape and preceding motion history; the prediction head maps its embedding to γ, α, and κ.Source: §4.2.5 · p. 9
4.2.6 Parameter Prediction and Scaling
- A two-layer prediction head with SiLU activation and dropout produces normalized γ, α, and κ values in [0, 1], mapped respectively to [1, 100], [1, 100], and [0, 1]; γ and α use logarithmic scaling, while κ is scaled linearly.Source: §4.2.6 · p. 9
4.2.7 Virtual-Material Dataset
- The virtual-material dataset samples 200 materials and simulates ten informative needle motions per material, with trajectories split by material into training, validation, and test sets.Each trajectory has 400 time steps; the split is 140 training, 30 validation, and 30 test materials.Source: §4.2.7 · p. 9, Figure 3 · p. 10
4.2.8 Supervised Parameter Training
- Training pairs different motions from the same material and combines parameter supervision with a cross-motion consistency loss to target material-dependent parameters.The objective is Lsup = Lparam + λconsLcons.Source: §4.2.8 · pp. 9–10
4.2.9 Differentiable Multi-Step QP Fine-Tuning
The model is fine-tuned by propagating trajectory error through a differentiable QP simulator over 12 steps. For long videos, overlapping-window parameter estimates are averaged, assuming the thread material remains constant.
4.2.9 Differentiable Multi-Step QP Fine-Tuning
- Differentiable QP rollouts propagate tracking error through 12 simulation steps to fine-tune predicted parameters against observed thread motion.The rollout begins from an observed configuration and uses observed needle inputs; this trajectory loss was introduced during fine-tuning after initial use was computationally intensive and produced unstable gradients.Source: §4.2.9 · pp. 10–11
4.2.10 Video-Level Parameter Inference
- Video-level parameters are formed by averaging normalized estimates from overlapping windows, assuming material and model parameters remain constant throughout the video.Windows contain at most 400 frames, begin 200 frames apart, and supply every second frame to the network.Source: §4.2.10 · p. 11
5 Results
On a held-out asymmetric flower-petal motion, the configured model reproduced the simulated silk reference trajectory with a mean node error of 0.194 mm. The experiments also report high trace success and parameter estimates that can be affected by centerline noise.
5.0.1 Reference Model and Evaluation Motion
- Evaluation used new virtual materials and an asymmetric flower-petal motion absent from training, selected to provide a sufficiently complex excitation.The motion comprises a forward sweep, variable-speed reverse, and return to the initial position.Source: §5.0.1 · p. 11, Figure 4 · p. 12
5.0.2 Thread Reconstruction from Video
- Ordered centerlines were obtained with a 99.92% trace success rate, although occasional segmentation and tracing errors displaced nodes and introduced temporal jitter.Source: §5.0.2 · p. 13
5.0.3 Automatic Model Configuration
- Using ground-truth centerlines, the CNN identified near-correct parameters that produced very low QP replay errors, while U-Net centerline noise sometimes degraded parameter predictions.Despite parameter mismatch, tracking error remained low and comparable in many cases.Source: §5.0.3 · p. 13
5.0.4 Trajectory Reconstruction
- 0.194 mm mean node error: the predicted model reproduced the simulated silk reference trajectory during the asymmetric flower-petal motion.Figure 5 shows representative configurations, with ground truth in black, U-Net observations in cyan, and model predictions in magenta.Source: Figure 5 · p. 12, §5.0.4 · p. 13
6 Conclusion
The framework automatically configures a CBF–CLF–QP suture-thread model from video and reproduces observed behavior on a held-out trajectory without manual parameter tuning. The paper identifies visual tracking errors and insufficiently informative excitation as limitations.
6 Conclusion
- The video-to-model pipeline reconstructs ordered thread nodes, estimates one parameter set from thread and needle motion, and simulates thread dynamics without manual tuning.Experiments report low error on a held-out trajectory.Source: §6 · p. 13
- Tracking errors can distort node observations and parameter predictions, while some excitation trajectories do not uniquely distinguish parameter effects.Future work includes more robust tracking, parameter-sensitive excitation, and tests with real suture threads of different materials.Source: §6 · p. 13