DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes
| Autor: | Michael Strecke, Joerg Stueckler |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Science - Robotics Computer Science - Graphics Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Robotics (cs.RO) Graphics (cs.GR) ComputingMethodologies_COMPUTERGRAPHICS Machine Learning (cs.LG) |
| Zdroj: | 2021 International Conference on 3D Vision (3DV). |
| DOI: | 10.1109/3dv53792.2021.00020 |
| Popis: | Differentiable physics is a powerful tool in computer vision and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited to objects with simple shape or shapes that are known in advance. In this paper, we propose a novel approach to differentiable physics with frictional contacts which represents object shapes implicitly using signed distance fields (SDFs). Our simulation supports contact point calculation even when the involved shapes are nonconvex. Moreover, we propose ways for differentiating the dynamics for the object shape to facilitate shape optimization using gradient-based methods. In our experiments, we demonstrate that our approach allows for model-based inference of physical parameters such as friction coefficients, mass, forces or shape parameters from trajectory and depth image observations in several challenging synthetic scenarios and a real image sequence. Comment: 22 pages, 23 Figures (including supplementary material). Presented 3DV 2021. Project website: https://diffsdfsim.is.tue.mpg.de/ |
| Databáze: | OpenAIRE |
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