PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
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Updated
Apr 24, 2024 - Python
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
PyTorch implementation of Pointnet2/Pointnet++
[NeurIPS'22] PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
Semantic3D segmentation with Open3D and PointNet++
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
[CVPR 2020, Oral] Category-Level Articulated Object Pose Estimation
Pointnet++ modules implemented as tensorflow 2 keras layers.
A PyTorch Implementation of Pointnet++.
A pointnet++ fork, with focus on semantic segmentation of differents datasets
PAPC is a deep learning for point clouds platform based on pure PaddlePaddle
This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
A clean PointNet++ segmentation model implementation. Support batch of samples with different number of points.
Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
Applying RandAugment on PointNet++
Code and Data for the paper "LPF-Defense: 3D Adversarial Defense based on Frequency Analysis", PLoS ONE
Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction
A pytorch implementation of PointNet and PointNet++
Semantic segmentation of LIDAR point clouds from the KITTI-360 dataset using a modified PointNet2. This is a Python and PyTorch based implementation using Jupyter Notebooks.
Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.
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