Point Cloud Generation
44 papers with code • 4 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Point Cloud Generation
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ShapeNet
JetClass
Most implemented papers
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
Generating 3D point clouds is challenging yet highly desired.
Deep Generative Modeling of LiDAR Data
pclucas14/lidar_generation
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In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.
Learning Localized Generative Models for 3D Point Clouds via Graph Convolution
diegovalsesia/GraphCNN-GAN
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ICLR 2019
We also study the problem of defining an upsampling layer in the graph-convolutional generator, such that it learns to exploit a self-similarity prior on the data distribution to sample more effectively.
LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving
Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and model in closed forms.
Spectral-GANs for High-Resolution 3D Point-cloud Generation
Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners.
A Rotation-Invariant Framework for Deep Point Cloud Analysis
nini-lxz/Rotation-Invariant-Point-Cloud-Analysis
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Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations.
Energy-Based Processes for Exchangeable Data
google-research/google-research
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ICML 2020
Recently there has been growing interest in modeling sets with exchangeability such as point clouds.
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
fei960922/GPointNet
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CVPR 2021
We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network.
SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds
ANLGBOY/SoftFlow
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NeurIPS 2020
Flow-based generative models are composed of invertible transformations between two random variables of the same dimension.
Conditional Set Generation with Transformers
An example of such a generator is the DeepSet Prediction Network (DSPN).
