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HSGAN: Hierarchical Graph Learning for Point Cloud Generation

https://doi.org/10.1109/tip.2021.3073318
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01225-0_4openalex-snapshot
Local Spectral Graph Convolution for Point Set Feature Learning
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_7openalex-snapshot
Multiresolution Tree Networks for 3D Point Cloud Processing
inferredcites →https://doi.org/10.1007/978-3-658-40442-0_9openalex-snapshot
Generative Adversarial Nets
inferredcites →https://doi.org/10.1109/cvpr.2015.7298801openalex-snapshot
3D ShapeNets: A deep representation for volumetric shapes
inferredcites →https://doi.org/10.1109/cvpr.2016.609openalex-snapshot
Volumetric and Multi-view CNNs for Object Classification on 3D Data
inferredcites →https://doi.org/10.1109/cvpr.2017.11openalex-snapshot
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-snapshot
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2017.264openalex-snapshot
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
inferredcites →https://doi.org/10.1109/cvpr.2017.701openalex-snapshot
OctNet: Learning Deep 3D Representations at High Resolutions
inferredcites →https://doi.org/10.1109/cvpr.2018.00029openalex-snapshot
FoldingNet: Point Cloud Auto-Encoder via Deep Grid Deformation
inferredcites →https://doi.org/10.1109/cvpr.2018.00478openalex-snapshot
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling
inferredcites →https://doi.org/10.1109/cvpr.2019.00047openalex-snapshot
TopNet: Structural Point Cloud Decoder
inferredcites →https://doi.org/10.1109/cvpr.2019.00054openalex-snapshot
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00104openalex-snapshot
Structural Relational Reasoning of Point Clouds
inferredcites →https://doi.org/10.1109/cvpr.2019.00110openalex-snapshot
3D Point Capsule Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00344openalex-snapshot
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling
inferredcites →https://doi.org/10.1109/cvpr.2019.00760openalex-snapshot
A-CNN: Annularly Convolutional Neural Networks on Point Clouds
inferredcites →https://doi.org/10.1109/cvpr.2019.00985openalex-snapshot
PointConv: Deep Convolutional Networks on 3D Point Clouds
inferredcites →https://doi.org/10.1109/cvpr.2019.00986openalex-snapshot
Octree Guided CNN With Spherical Kernels for 3D Point Clouds
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00797openalex-snapshot
C-Flow: Conditional Generative Flow Models for Images and 3D Point Clouds
inferredcites →https://doi.org/10.1109/iccv.2017.556openalex-snapshot
3D Graph Neural Networks for RGBD Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2019.00396openalex-snapshot
3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions
inferredcites →https://doi.org/10.1109/iccv.2019.00464openalex-snapshot
PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows
inferredcites →https://doi.org/10.1109/iccv.2019.00534openalex-snapshot
DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing
inferredcites →https://doi.org/10.1109/iccv.2019.00730openalex-snapshot
PU-GAN: A Point Cloud Upsampling Adversarial Network
inferredcites →https://doi.org/10.1109/iros.2015.7353481openalex-snapshot
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
inferredcites →https://doi.org/10.1109/jproc.2018.2820126openalex-snapshot
Graph Signal Processing: Overview, Challenges, and Applications
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tpami.2020.2982166openalex-snapshot
Deep Learning for Image Super-Resolution: A Survey
inferredcites →https://doi.org/10.1109/tsp.2014.2321121openalex-snapshot
Discrete Signal Processing on Graphs: Frequency Analysis
inferredcites →https://doi.org/10.1109/tsp.2017.2771730openalex-snapshot
Fast Resampling of Three-Dimensional Point Clouds via Graphs
inferredcites →https://doi.org/10.1109/wacv45572.2020.9093430openalex-snapshot
PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention
inferredcites →https://doi.org/10.1145/3072959.3073608openalex-snapshot
O-CNN
inferredcites →https://doi.org/10.1145/3240508.3240621openalex-snapshot
RGCNN
inferredcites →https://doi.org/10.1145/3326362openalex-snapshot
Dynamic Graph CNN for Learning on Point Clouds
inferredcites →https://doi.org/10.18653/v1/d16-1053openalex-snapshot
Long Short-Term Memory-Networks for Machine Reading
inferredcites →https://doi.org/10.18653/v1/d16-1244openalex-snapshot
A Decomposable Attention Model for Natural Language Inference
inferredcites →https://doi.org/10.3156/jsoft.29.5_177_2openalex-snapshot
GAN（Generative Adversarial Nets）
inferredcites →https://doi.org/10.3929/ethz-b-000223162openalex-snapshot
Stabilizing Training of Generative Adversarial Networks through Regularization
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1506.05163openalex-snapshot
Deep Convolutional Networks on Graph-Structured Data
inferredcites →https://doi.org/10.48550/arxiv.1512.03012openalex-snapshot
ShapeNet: An Information-Rich 3D Model Repository
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-snapshot
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.48550/arxiv.1610.07584openalex-snapshot
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
inferredcites →https://doi.org/10.48550/arxiv.1705.07215openalex-snapshot
On Convergence and Stability of GANs
inferredcites →https://doi.org/10.48550/arxiv.1707.02392openalex-snapshot
Learning Representations and Generative Models for 3D Point Clouds
inferredcites →https://doi.org/10.48550/arxiv.1801.04406openalex-snapshot
Which Training Methods for GANs do actually Converge?
inferredcites →https://doi.org/10.48550/arxiv.1904.08082openalex-snapshot
Self-Attention Graph Pooling
inferredcites →https://doi.org/10.48550/arxiv.1904.10014openalex-snapshot
Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
inferredcites →https://doi.org/10.5281/zenodo.10089705openalex-snapshot
ShapeNet: An Information-Rich 3D Model Repository
inferredcites →https://doi.org/10.5281/zenodo.1318390openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
inferredcites →https://doi.org/10.65215/2q58a426openalex-snapshot
Attention Is All You Need

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1007/978-3-031-72855-6_25openalex-snapshot
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation
inferred← citeshttps://doi.org/10.1007/978-981-95-0648-4_9openalex-snapshot
Semantic Instance Completion of 3D Point Cloud Scenes
inferred← citeshttps://doi.org/10.1007/978-981-97-0068-4_48openalex-snapshot
SE(3)-Diffusion: An Equivariant Diffusion Model for 3D Point Cloud Generation
inferred← citeshttps://doi.org/10.1007/s10044-023-01155-xopenalex-snapshot
SE-MD: a single-encoder multiple-decoder deep network for point cloud reconstruction from 2D images
inferred← citeshttps://doi.org/10.1007/s11042-023-15541-4openalex-snapshot
Learning graph-based representations for scene flow estimation
inferred← citeshttps://doi.org/10.1007/s41095-022-0321-5openalex-snapshot
A survey of deep learning-based 3D shape generation
inferred← citeshttps://doi.org/10.1016/j.advengsoft.2025.103996openalex-snapshot
As-scanned point cloud generation using structured-light simulation and machine learning-based coverage prediction
inferred← citeshttps://doi.org/10.1016/j.aei.2026.105222openalex-snapshot
MGMR-Net: 2D-Guided Multi-Scale Graph-Manifold regularization network for Multi-Class pavement distress and surface feature segmentation from 3D laser data
inferred← citeshttps://doi.org/10.1016/j.compag.2025.111062openalex-snapshot
Multispectral image reconstruction from RGB image for maize growth status monitoring based on window-adaptive spatial-spectral attention transformer
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ICDDPM: Image-conditioned denoising diffusion probabilistic model for real-world complex point cloud single view reconstruction
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Deep-learning-based point cloud completion methods: A review
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SD-GCN: Saliency-based dilated graph convolution network for pavement crack extraction from 3D point clouds
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A novel single-view point cloud generation model for multimodal spatial representation
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Evaluation of Class Distribution and Class Combinations on Semantic Segmentation of 3D Point Clouds With PointNet
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MGE-Net: Task-oriented Point Cloud Sampling based on Multi-scale Geometry Estimation
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SPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation
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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II)
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TGNet: Learning 3D Shape from Sparse and Incomplete Point Cloud
inferred← citeshttps://doi.org/10.1109/tip.2022.3142569openalex-snapshot
2021 Index IEEE Transactions on Image Processing Vol. 30
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Dual-View 3D Reconstruction via Learning Correspondence and Dependency of Point Cloud Regions
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PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling
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WSSIC-Net: Weakly-Supervised Semantic Instance Completion of 3D Point Cloud Scenes
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CompletionMamba: Taming State Space Model for Point Cloud Completion
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Cross-Modality Feature Aggregation for Cross-Domain Point Cloud Representation Learning
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Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
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A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
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Arbitrary-Scale Point Cloud Upsampling With Saliency-Aware Implicit Surface Guidance
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Intelligent Generation of Graphical Game Assets: A Conceptual Framework and Systematic Review of the State of the Art
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Machine learning-based 3D scan coverage estimation for smart control applications
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