Tracking deformable objects with point clouds

By John Schulman, Alex X. Lee, Jonathan Ho, Pieter Abbeel. 2013

195 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Latent Space Physics: Towards Learning the Temporal Evolution of Fluid Flow (2018, background): “While physical properties and interactions of objects have long been of interest for computer vision problems [24, 34, 13], addressing physics problems with deep learning algorithms is an area of particular interest in recent years.”
DART: Dense Articulated Real-Time Tracking (2014, background): “However, the full generality of these models comes at the cost of increased model complexity, and for many objects that are well modelled as piecewise rigid bodies, such overparameterized output obscures the utility of tracking the articulated body state directly.”
Learning to Manipulate Deformable Objects without Demonstrations (2019, background): “Although this produces better behavior, transferring it to a robot involves solving the difficult state-estimation problem [51, 52].”
Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects (2019, methodology): “Thus our method does not make any assumption about the distribution of rope colors or background colors, which was used in many previous works [12], [14]–[16].”
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning (2015, background): “Note that in the fields of computer vision and robotics, there have been studies on predicting physical interactions or inferring 3D properties of objects for various purposes including 3D reasoning [6, 13] and tracking [9].”
Modeling, learning, perception, and control methods for deformable object manipulation (2021, methodology): “Stereo and depth cameras are used for extracting exact 3D geometry and texture, as well as tracking of deformation (17, 23, 35, 43, 44).”
Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey (2018, methodology): “A classiﬁcation of the reviewed approaches is summarized in Table 3.”
Constraining dense hand surface tracking with elasticity (2020, background): “Schulman et al. [2013] derive a similar probabilistic approach to track rods and flat sheets of material.”
Reasoning About Liquids via Closed-Loop Simulation (2017, background): “There has also been work showing it is possible to connect the two for some deformable objects [28].”