Generating 3D Point Clouds

6 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Adversarial Autoencoders for Compact Representations of 3D Point Clouds

MaciejZamorski/3d-AAE 19 Nov 2018

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

gmum/3d-point-clouds-HyperCloud ICML 2020

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

syb7573330/PointGrow 12 Oct 2018

Generating 3D point clouds is challenging yet highly desired.

Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds

Vahe1994/ThreeDLAPGAN 13 Dec 2019

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

klarh/flowws-keras-geometry 5 Oct 2021

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

openai/point-e 16 Dec 2022

This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes.