Point Cloud Generation

29 papers with code • 3 benchmarks • 1 datasets

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Most implemented papers

PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows

stevenygd/PointFlow ICCV 2019

Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.

Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction

chenhsuanlin/3D-point-cloud-generation 21 Jun 2017

Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones.

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.

3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions

prajwalsingh/TreeGCN-ED ICCV 2019

In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN.

Conditional Invertible Flow for Point Cloud Generation

MStypulkowski/CIF 16 Oct 2019

This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.

Diffusion Probabilistic Models for 3D Point Cloud Generation

luost26/diffusion-point-cloud CVPR 2021

We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.

SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data

jw9730/setvae CVPR 2021

Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.

RealPoint3D: Point Cloud Generation from a Single Image with Complex Background

Yan-Xia/RealPoint3D 8 Sep 2018

Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud.

PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

syb7573330/PointGrow 12 Oct 2018

Generating 3D point clouds is challenging yet highly desired.

Deep Generative Modeling of LiDAR Data

pclucas14/lidar_generation 4 Dec 2018

In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map.