用3D高斯点阵追踪可变形长条物体的三维形状，结合视觉与机械臂状态。
DLO-Splatting: Tracking Deformable Linear Objects Using 3D Gaussian Splatting
- 基于位置的动力学模型预测物体形状，加入平滑与刚性修正。
- 通过3D高斯点阵渲染损失迭代优化，对齐多视角图像观测。
- 适合需要精确形变建模的机器人操作任务，如打结。
本文提出DLO-Splatting算法，通过多视角RGB图像与夹持器状态信息，估计可变形长条物体（DLO）的三维形状。该算法采用基于位置的动力学模型，结合形状平滑性和刚性衰减校正进行形状预测；在更新步骤中，利用基于3D高斯点阵的渲染损失进行优化，迭代渲染并精修预测结果，使其与视觉观测对齐。初步实验在打结场景中取得良好效果，该场景对现有纯视觉方法极具挑战性。
原文摘要 · Abstract (English)
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.
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