DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting
This addresses the problem of precise 3D shape tracking for deformable objects in robotics, particularly for tasks like knot tying, but appears incremental as it builds on existing techniques like 3D Gaussian Splatting.
The paper tackles the problem of estimating the 3D shape of Deformable Linear Objects (DLOs) from multi-view RGB images and gripper state information, achieving promising results in a knot tying scenario that is challenging for existing methods.
This work presents DLO-Splatting, an algorithm for estimating the 3D shape of Deformable Linear Objects (DLOs) from multi-view RGB images and gripper state information through prediction-update filtering. The DLO-Splatting algorithm uses a position-based dynamics model with shape smoothness and rigidity dampening corrections to predict the object shape. Optimization with a 3D Gaussian Splatting-based rendering loss iteratively renders and refines the prediction to align it with the visual observations in the update step. Initial experiments demonstrate promising results in a knot tying scenario, which is challenging for existing vision-only methods.