The resulting high-dimensional implicit function can be differentiated with respect to the input poses and thus can be used to project arbitrary poses onto the manifold by using gradient descent on the set of 3-dimensional hyperspheres.
The core of our method are TOCH fields, a novel spatio-temporal representation for modeling correspondences between hands and objects during interaction.
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception.
We present a 3D capsule module for processing point clouds that is equivariant to 3D rotations and translations, as well as invariant to permutations of the input points.
This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer.
Ranked #2 on Surface Normals Estimation on PCPNet
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #4 on Graph Classification on REDDIT-B
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #3 on Graph Classification on NCI1
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #3 on Node Classification on Cora