Search Results for author:
Found 8 papers, 5 papers with code
Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not.
Hamiltonian GAN
In this work, we present a GAN-based video generation pipeline with a learned configuration space map and Hamiltonian neural network motion model, to learn a representation of the configuration space from data.
Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body
In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not.
Joint Estimation of Image Representations and their Lie Invariants
In both approaches, the underlying dynamics of the image sequence are modelled explicitly to disentangle them from the image representations.
LagNetViP: A Lagrangian Neural Network for Video Prediction
In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.
Equivariant Multi-View Networks
Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views.
Learning SO(3) Equivariant Representations with Spherical CNNs
We address the problem of 3D rotation equivariance in convolutional neural networks.
Polar Transformer Networks
The result is a network invariant to translation and equivariant to both rotation and scale.
