PyTorch Volume Models for 3D data
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Updated
Aug 1, 2024 - Python
PyTorch Volume Models for 3D data
Set of models for classifcation of 3D volumes
official code of “OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding”
The code for the paper "Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models" (ICCV'23).
NNDL Project - 3D Object Classification
PyTorch implementation of "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
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.
MeshRunner - Improved classification of 3D mesh objects
This repository is for the Deep learning(Classification & XAI) part with 3D OCTA volumes.
This is the official repository of the original Point Transformer architecture.
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')
This package implements deep learning modules for medical imaging application in PyTorch (miTorch).
🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey
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.
Implementation of Multi-view CNN for 3D classification evaluated on MoodleNet40 dataset.
Solution of team tara: Public 7th, Private 13th (The renewed pipeline scores 8th place)
Automated detection of focal cortical dysplasia
3D Face Classification with Graph Neural Networks
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).
3D MNIST Point Cloud Classifier using 3D ConvNet with Swift for TensorFlow
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