Generating 3D Point Clouds
6 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
Hypernetwork approach to generating point clouds
The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
Generating 3D point clouds is challenging yet highly desired.
Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds
Constructing high-quality generative models for 3D shapes is a fundamental task in computer vision with diverse applications in geometry processing, engineering, and design.
Geometric Algebra Attention Networks for Small Point Clouds
Much of the success of deep learning is drawn from building architectures that properly respect underlying symmetry and structure in the data on which they operate - a set of considerations that have been united under the banner of geometric deep learning.
Point-E: A System for Generating 3D Point Clouds from Complex Prompts
This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.