Summary of Predicting 3d Rigid Body Dynamics with Deep Residual Network, by Abiodun Finbarrs Oketunji
Predicting 3D Rigid Body Dynamics with Deep Residual Network
by Abiodun Finbarrs Oketunji
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study applies deep residual networks to predict the dynamics of interacting three-dimensional rigid bodies, combining a C++ physics simulator with a PyTorch-built model. The framework generates training data simulating linear and angular motion, collisions, fluid friction, gravity, and damping. A deep residual network is designed to handle 3D complexities, consisting of an input layer, multiple residual blocks, and an output layer. Evaluating the network’s performance using 10,000 simulated scenarios with 3-5 interacting rigid bodies, it achieves a mean squared error of 0.015 for position predictions and 0.022 for orientation predictions, improving baseline methods by 25%. The results demonstrate the model’s ability to capture physical interactions, particularly in elastic collisions and rotational dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses deep learning to predict how objects move and interact with each other. It combines a physics simulator with a special type of neural network called a residual network. The network is trained on data that simulates different types of motion, such as linear and angular movement, collisions, and friction. The results show that the network can accurately predict how objects will move and interact, even in complex situations like elastic collisions and rotational dynamics. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Residual network
