🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey

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

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

official code of “OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding”

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')

Set of models for classifcation of 3D volumes

A new approach for retrieval and classification of 3D models that directly performs in the CAD format without any format conversion to other representations like point clouds of meshes, thus avoiding any loss of information.

This is the official repository of the original Point Transformer architecture.

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

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

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).

3D Face Classification with Graph Neural Networks

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

PyTorch Volume Models for 3D data

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.

ModelNet10 classification method based on rendered videos.

The code for the paper "Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models" (ICCV'23).