Occlusion-robust Deformable Object Tracking without Physics Simulation

TL;DR

This paper introduces an occlusion-robust RGBD tracking framework for deformable objects that does not rely on physics simulation, using regularization, free-space reasoning, and shape descriptors to improve accuracy during occlusions.

Contribution

The proposed method combines regularization, free-space reasoning, and shape descriptors to enhance deformable object tracking robustness without physics simulation.

Findings

Improved accuracy in occlusion scenarios compared to physics-based methods

Maintains real-time performance during tracking

Effectively detects tracking failures during occlusion

Abstract

Estimating the state of a deformable object is crucial for robotic manipulation, yet accurate tracking is challenging when the object is partially-occluded. To address this problem, we propose an occlusion-robust RGBD sequence tracking framework based on Coherent Point Drift (CPD). To mitigate the effects of occlusion, our method 1) Uses a combination of locally linear embedding and constrained optimization to regularize the output of CPD, thus enforcing topological consistency when occlusions create disconnected pieces of the object; 2) Reasons about the free-space visible by an RGBD sensor to better estimate the prior on point location and to detect tracking failures during occlusion; and 3) Uses shape descriptors to find the most relevant previous state of the object to use for tracking after a severe occlusion. Our method does not rely on physics simulation or a physical model of the object, which can be difficult to obtain in unstructured environments. Despite having no physical model, our experiments demonstrate that our method achieves improved accuracy in the presence of occlusion as compared to a physics-based CPD method while maintaining adequate run-time.