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Physics-Based Simulation of Soft-Body Deformation Using RGB-D Data

Providing real-time interaction in an immersive environment has drawn considerable attention in the virtual training fields. Physics-based simulations are suitable for such environments; however, they require the definition and adjustment of coefficients that determine material properties, making th...

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Detalles Bibliográficos
Autores principales: Kang, Daeun, Moon, Jaeseok, Yang, Saeyoung, Kwon, Taesoo, Kim, Yejin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571817/
https://www.ncbi.nlm.nih.gov/pubmed/36236323
http://dx.doi.org/10.3390/s22197225
Descripción
Sumario:Providing real-time interaction in an immersive environment has drawn considerable attention in the virtual training fields. Physics-based simulations are suitable for such environments; however, they require the definition and adjustment of coefficients that determine material properties, making the methods more complex and time-consuming. In this paper, we introduce a novel approach to simulating the soft-body deformation of an observed object. Using an off-the-shelf RGB-D sensor, the proposed approach tracks an object’s movement and simulates its deformation in an iterative manner. Polygonal models with different resolutions are used to improve the simulation speed and visual quality. During the simulation process, a low-resolution model is used for surface deformation using neighboring feature points detected from the sensor, and a volumetric model is added for internal force estimation. To visualize the observed model in detail, the deformed and low-resolution model is mapped to a high-resolution model using mean value coordinate interpolation. To handle topological deformations, such as cutting or tearing, a part intersected by a cutting tool is recognized by the sensor and responds to external forces. As shown in the experimental results, our approach generates convincing deformations of observed objects in real time.