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Capturing Deformations of Interacting Non-rigid Objects Using RGB-D Data

Abstract : This paper presents a method for tracking multiple interacting deformable objects undergoing rigid motions, elastic deformations and contacts, using image and point cloud data provided by an RGB-D sensor. A joint registration framework is proposed, based on physical Finite Element Method (FEM) elastic and interaction models. It first relies on a visual segmentation of the considered objects in the RGB images. The different segmented point clouds are then processed to estimate rigid transformations with on an ICP algorithm, and to determine geometrical point-to-point correspondences with the meshes. External forces resulting from these correspondences and between the current and the rigidly transformed mesh can then be derived. It provides both non-rigid and rigid data cues. A classical collision detection and response model is also integrated, giving contact forces between the objects. The deformations of the objects are estimated by solving a dynamic system balancing these external and contact forces with the internal or regularization forces computed through the FEM elastic model. This approach has been here tested on different scenarios involving two or three interacting deformable objects of various shapes, with promising results.
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Submitted on : Monday, October 8, 2018 - 1:06:50 AM
Last modification on : Friday, February 4, 2022 - 3:09:23 AM
Long-term archiving on: : Wednesday, January 9, 2019 - 1:16:00 PM


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  • HAL Id : hal-01889771, version 1



Antoine Petit, Stéphane Cotin, Vincenzo Lippiello, Bruno Siciliano. Capturing Deformations of Interacting Non-rigid Objects Using RGB-D Data. IROS 2018 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2018, Madrid, Spain. ⟨hal-01889771⟩



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