A pytorch implementation of PointNet and PointNet++
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Updated
Sep 16, 2024 - Python
A pytorch implementation of PointNet and PointNet++
Code for "Deep Learning for Detecting Trees in the Urban Environment from Lidar"
[CVPR 2020, Oral] Category-Level Articulated Object Pose Estimation
Semantic3D segmentation with Open3D and PointNet++
GraspNet and Pointnet2/PointNet++ PyTorch Upgrade (v1.7.1 -> v1.13.1)
MSc thesis work conducted @ MBZUAI. The work was presented and published at VISAPP 2023 conference.
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
Official Code for ICML 2021 paper "Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline"
Official implementation of the paper "Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space"
Code and Data for the paper "LPF-Defense: 3D Adversarial Defense based on Frequency Analysis", PLoS ONE
[NeurIPS 2019, Spotlight] Point-Voxel CNN for Efficient 3D Deep Learning
PAPC is a deep learning for point clouds platform based on pure PaddlePaddle
Prediction of vegetation coverage maps from High Density Lidar data, in a weakly supervised deep learning setting.
Frustum Pointnet Implementation on KITTI and Lyft Dataset
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.
Efficient Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction
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