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
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
stevenygd/PointFlow
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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.
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.
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
In this paper, we propose a novel generative adversarial network (GAN) for 3D point clouds generation, which is called tree-GAN.
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
chenhsuanlin/3D-point-cloud-generation
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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.
Diffusion Probabilistic Models for 3D Point Cloud Generation
luost26/diffusion-point-cloud
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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.
Conditional Invertible Flow for Point Cloud Generation
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales.
LION: Latent Point Diffusion Models for 3D Shape Generation
nv-tlabs/LION
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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.
Taming Transformers for Realistic Lidar Point Cloud Generation
Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during sampling.
RealPoint3D: Point Cloud Generation from a Single Image with Complex Background
Then, the image together with the retrieved shape model is fed into the proposed network to generate the fine-grained 3D point cloud.
