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Found 15 papers, 5 papers with code
Approximately Piecewise E(3) Equivariant Point Networks
Integrating a notion of symmetry into point cloud neural networks is a provably effective way to improve their generalization capability.
XCube ($\mathcal{X}^3$): Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
In addition to unconditional generation, we show that our model can be used to solve a variety of tasks such as user-guided editing, scene completion from a single scan, and text-to-3D.
Neural Kernel Surface Reconstruction
We present a novel method for reconstructing a 3D implicit surface from a large-scale, sparse, and noisy point cloud.
Neural LiDAR Fields for Novel View Synthesis
We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from novel viewpoints.
LION: Latent Point Diffusion Models for 3D Shape Generation
To advance 3D DDMs and make them useful for digital artists, we require (i) high generation quality, (ii) flexibility for manipulation and applications such as conditional synthesis and shape interpolation, and (iii) the ability to output smooth surfaces or meshes.
Ranked #1 on
Point Cloud Generation
on ShapeNet Airplane
A Bibliographic View on Constrained Clustering
A keyword search on constrained clustering on Web-of-Science returned just under 3, 000 documents.
Learning Smooth Neural Functions via Lipschitz Regularization
The latent descriptor of a neural field acts as a deformation handle for the 3D shape it represents.
Neural Fields as Learnable Kernels for 3D Reconstruction
We present Neural Kernel Fields: a novel method for reconstructing implicit 3D shapes based on a learned kernel ridge regression.
Symmetry Breaking in Symmetric Tensor Decomposition
In this note, we consider the highly nonconvex optimization problem associated with computing the rank decomposition of symmetric tensors.
Human 3D keypoints via spatial uncertainty modeling
We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint.
Neural Splines: Fitting 3D Surfaces with Infinitely-Wide Neural Networks
We present Neural Splines, a technique for 3D surface reconstruction that is based on random feature kernels arising from infinitely-wide shallow ReLU networks.
VoronoiNet: General Functional Approximators with Local Support
Voronoi diagrams are highly compact representations that are used in various Graphics applications.
Gradient Dynamics of Shallow Univariate ReLU Networks
We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network function.
ABC: A Big CAD Model Dataset For Geometric Deep Learning
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications.
Deep Geometric Prior for Surface Reconstruction
We propose the use of a deep neural network as a geometric prior for surface reconstruction.
