On the public citation graph

3d Point Cloud Representation Learning Via Dual Curve-Graph Feature Fusion

https://doi.org/10.2139/ssrn.4894161
0 signed · 40 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-01237-3_6openalex-extracted
SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters
inferredcites →https://doi.org/10.1007/978-3-030-58580-8_34openalex-extracted
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding
inferredcites →https://doi.org/10.1007/978-3-031-19842-7_2openalex-extracted
ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection
inferredcites →https://doi.org/10.1016/j.aei.2021.101501openalex-extracted
3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review
inferredcites →https://doi.org/10.1016/j.displa.2023.102456openalex-extracted
Deep learning-based 3D point cloud classification: A systematic survey and outlook
inferredcites →https://doi.org/10.1016/j.gmod.2023.101188openalex-extracted
Joint Data and Feature Augmentation for Self-Supervised Representation Learning on Point Clouds
inferredcites →https://doi.org/10.1016/j.imavis.2023.104890openalex-extracted
Feature extraction and representation learning of 3D point cloud data
inferredcites →https://doi.org/10.1016/j.neunet.2024.106158openalex-extracted
Unsupervised distribution-aware keypoints generation from 3D point clouds
inferredcites →https://doi.org/10.1016/j.patcog.2023.109800openalex-extracted
MKConv: Multidimensional feature representation for point cloud analysis
inferredcites →https://doi.org/10.1109/3dv.2019.00051openalex-extracted
Unsupervised Feature Learning for Point Cloud Understanding by Contrasting and Clustering Using Graph Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2015.7298801openalex-extracted
3D ShapeNets: A deep representation for volumetric shapes
inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-extracted
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2018.00479openalex-extracted
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
inferredcites →https://doi.org/10.1109/cvpr.2019.00344openalex-extracted
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01112openalex-extracted
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00967openalex-extracted
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
inferredcites →https://doi.org/10.1109/iccv.2019.00167openalex-extracted
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
inferredcites →https://doi.org/10.1109/iccv48922.2021.00095openalex-extracted
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
inferredcites →https://doi.org/10.1109/iccv48922.2021.00604openalex-extracted
Pyramid Point Cloud Transformer for Large-Scale Place Recognition
inferredcites →https://doi.org/10.1109/iccv48922.2021.00647openalex-extracted
Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds
inferredcites →https://doi.org/10.1109/iccv48922.2021.00964openalex-extracted
Unsupervised Point Cloud Pre-training via Occlusion Completion
inferredcites →https://doi.org/10.1109/iccv48922.2021.01009openalex-extracted
Self-Supervised Pretraining of 3D Features on any Point-Cloud
inferredcites →https://doi.org/10.1109/iccv51070.2023.00761openalex-extracted
Clustering based Point Cloud Representation Learning for 3D Analysis
inferredcites →https://doi.org/10.1109/mmsp.2017.8122237openalex-extracted
Towards subjective quality assessment of point cloud imaging in augmented reality
inferredcites →https://doi.org/10.1109/tcsvt.2022.3207789openalex-extracted
PointFilterNet: A Filtering Network for Point Cloud Denoising
inferredcites →https://doi.org/10.1109/tits.2022.3198836openalex-extracted
3DCTN: 3D Convolution-Transformer Network for Point Cloud Classification
inferredcites →https://doi.org/10.1109/tnnls.2021.3132836openalex-extracted
Rethinking 3-D LiDAR Point Cloud Segmentation
inferredcites →https://doi.org/10.1109/tnnls.2022.3155282openalex-extracted
A Novel Local–Global Graph Convolutional Method for Point Cloud Semantic Segmentation
inferredcites →https://doi.org/10.1109/tnnls.2022.3162301openalex-extracted
Dual-Graph Attention Convolution Network for 3-D Point Cloud Classification
inferredcites →https://doi.org/10.1109/tpami.2021.3063611openalex-extracted
Towards A Weakly Supervised Framework for 3D Point Cloud Object Detection and Annotation
inferredcites →https://doi.org/10.1145/2980179.2980238openalex-extracted
A scalable active framework for region annotation in 3D shape collections
inferredcites →https://doi.org/10.1145/3326362openalex-extracted
Dynamic Graph CNN for Learning on Point Clouds
inferredcites →https://doi.org/10.1145/3451394openalex-extracted
3DGCN: 3-Dimensional Dynamic Graph Convolutional Network for Citywide Crowd Flow Prediction
inferredcites →https://doi.org/10.1145/3474085.3475458openalex-extracted
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
inferredcites →https://doi.org/10.1145/3526114.3558636openalex-extracted
Point Cloud Capture and Editing for AR Environmental Design
inferredcites →https://doi.org/10.1609/aaai.v33i01.33018376openalex-extracted
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
inferredcites →https://doi.org/10.1609/aaai.v33i01.33018778openalex-extracted
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-Based Sequence to Sequence Network
inferredcites →https://doi.org/10.1609/aaai.v35i4.16414openalex-extracted
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
inferredcites →https://doi.org/10.48550/arxiv.1706.02413openalex-extracted
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
inferredcites →https://doi.org/10.48550/arxiv.2207.12654openalex-snapshot
ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.