There are 3 repositories under 3d-classification topic.
🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey
Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)
PyTorch implementation of "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
The PyTorch re-implement of a 3D CNN Tracker to extract coronary artery centerlines with state-of-the-art (SOTA) performance. (paper: 'Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classiﬁer')
Weakly supervised 3D classification of multi-disease chest CT scans using multi-resolution deep segmentation features via dual-stage CNN architecture (DenseVNet, 3D Residual U-Net).
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
This is the official repository of the original Point Transformer architecture.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
A fast and low memory requirement version of PointHop and PointHop++, which is built upon Apache Spark.
3D MNIST Point Cloud Classifier using 3D ConvNet with Swift for TensorFlow
3D Face Classification with Graph Neural Networks
Solution of team tara: Public 7th, Private 13th (The renewed pipeline scores 8th place)
This package implements deep learning modules for medical imaging application in PyTorch (miTorch).
ModelNet10 classification method based on rendered videos.
Implementation of Multi-view CNN for 3D classification evaluated on MoodleNet40 dataset.
Automated detection of focal cortical dysplasia
A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.
RandAugment process for point cloud data to handle with 3D classification task