🔥Deep Learning for 3D Point Clouds (IEEE TPAMI, 2020)

Code for A GRAPH-CNN FOR 3D POINT CLOUD CLASSIFICATION (ICASSP 2018)

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')

PyTorch implementation of "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593

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

PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification

ModelNet10 classification method based on rendered videos.

3D MNIST Point Cloud Classifier using 3D ConvNet with Swift for TensorFlow

Automated detection of focal cortical dysplasia

A fast and low memory requirement version of PointHop and PointHop++, which is built upon Apache Spark.

RandAugment process for point cloud data to handle with 3D classification task