A pointnet++ fork, with focus on semantic segmentation of differents datasets
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Updated
Jun 2, 2018 - Python
A pointnet++ fork, with focus on semantic segmentation of differents datasets
Applying RandAugment on PointNet++
A clean PointNet++ segmentation model implementation. Support batch of samples with different number of points.
A PyTorch Implementation of Pointnet++.
Pointnet++ modules implemented as tensorflow 2 keras layers.
✨ PointNet++ feature extractor and output heads implemented in TensorFlow 1.15 with Keras Models
This is the official pytorch implementation for paper: IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction
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.
Frustum Pointnet Implementation on KITTI and Lyft Dataset
Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.
PAPC is a deep learning for point clouds platform based on pure PaddlePaddle
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
[NeurIPS'22] PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
PyTorch implementation of Pointnet2/Pointnet++
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