  Existing shape estimation methods for deformable object manipulation suffer
from the drawbacks of being off-line, model dependent, noise-sensitive or
occlusion-sensitive, and thus are not appropriate for manipulation tasks
requiring high precision. In this paper, we present a real-time shape
estimation approach for autonomous robotic manipulation of 3D deformable
objects. Our method fulfills all the requirements necessary for the
high-quality deformable object manipulation in terms of being real-time,
model-free and robust to noise and occlusion. These advantages are accomplished
using a joint tracking and reconstruction framework, in which we track the
object deformation by aligning a reference shape model with the stream input
from the RGB-D camera, and simultaneously upgrade the reference shape model
according to the newly captured RGB-D data. We have evaluated the quality and
robustness of our real-time shape estimation pipeline on a set of deformable
manipulation tasks implemented on physical robots. Videos are available at
https://lifeisfantastic.github.io/DeformShapeEst/
