pytorch implementation for PointNet

Overview

PointNet.pytorch

This repo is implementation for PointNet(https://arxiv.org/abs/1612.00593) in pytorch. The model is in pointnet/model.py.

It is tested with pytorch-1.0.

Download data and running

git clone https://github.com/fxia22/pointnet.pytorch
cd pointnet.pytorch
pip install -e .

Download and build visualization tool

cd script
bash build.sh #build C++ code for visualization
bash download.sh #download dataset

Training

cd utils
python train_classification.py --dataset 
   
     --nepoch=
    
      --dataset_type 
     
      
python train_segmentation.py --dataset 
      
        --nepoch=
        
       
      
     
    
   

Use --feature_transform to use feature transform.

Performance

Classification performance

On ModelNet40:

Overall Acc
Original implementation 89.2
this implementation(w/o feature transform) 86.4
this implementation(w/ feature transform) 87.0

On A subset of shapenet

Overall Acc
Original implementation N/A
this implementation(w/o feature transform) 98.1
this implementation(w/ feature transform) 97.7

Segmentation performance

Segmentation on A subset of shapenet.

Class(mIOU) Airplane Bag Cap Car Chair Earphone Guitar Knife Lamp Laptop Motorbike Mug Pistol Rocket Skateboard Table
Original implementation 83.4 78.7 82.5 74.9 89.6 73.0 91.5 85.9 80.8 95.3 65.2 93.0 81.2 57.9 72.8 80.6
this implementation(w/o feature transform) 73.5 71.3 64.3 61.1 87.2 69.5 86.1 81.6 77.4 92.7 41.3 86.5 78.2 41.2 61.0 81.1
this implementation(w/ feature transform) 87.6 81.0

Note that this implementation trains each class separately, so classes with fewer data will have slightly lower performance than reference implementation.

Sample segmentation result: seg

Links

Owner
Fei Xia
PhD @StanfordVL @cvgl Computer Vision and Robotics
Fei Xia
A super lightweight Lagrangian model for calculating millions of trajectories using ERA5 data

Easy-ERA5-Trck Easy-ERA5-Trck Galleries Install Usage Repository Structure Module Files Version iteration Easy-ERA5-Trck is a super lightweight Lagran

Zhenning Li 26 Nov 19, 2022
The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch) Abstract Co-training, extended from self-training, is one of the frameworks for semi-su

cmc 6 Dec 04, 2022
code for our paper "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer"

SHOT++ Code for our TPAMI submission "Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer" that is ext

75 Dec 16, 2022
Waymo motion prediction challenge 2021: 3rd place solution

Waymo motion prediction challenge 2021: 3rd place solution 📜 Technical report 🗨️ Presentation 🎉 Announcement 🛆Motion Prediction Channel Website 🛆

158 Jan 08, 2023
SCAAML is a deep learning framwork dedicated to side-channel attacks run on top of TensorFlow 2.x.

SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framwork dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x.

Google 69 Dec 21, 2022
A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021)

GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i

4 Sep 13, 2022
MohammadReza Sharifi 27 Dec 13, 2022
CLUES: Few-Shot Learning Evaluation in Natural Language Understanding

CLUES: Few-Shot Learning Evaluation in Natural Language Understanding This repo contains the data and source code for baseline models in the NeurIPS 2

Microsoft 29 Dec 29, 2022
This is an open solution to the Home Credit Default Risk challenge 🏡

Home Credit Default Risk: Open Solution This is an open solution to the Home Credit Default Risk challenge 🏡 . More competitions 🎇 Check collection

minerva.ml 427 Dec 27, 2022
3D cascade RCNN for object detection on point cloud

3D Cascade RCNN This is the implementation of 3D Cascade RCNN: High Quality Object Detection in Point Clouds. We designed a 3D object detection model

Qi Cai 22 Dec 02, 2022
This is code of book "Learn Deep Learning with PyTorch"

深度学习入门之PyTorch Learn Deep Learning with PyTorch 非常感谢您能够购买此书,这个github repository包含有深度学习入门之PyTorch的实例代码。由于本人水平有限,在写此书的时候参考了一些网上的资料,在这里对他们表示敬意。由于深度学习的技术在

Xingyu Liao 2.5k Jan 04, 2023
Stock-Prediction - prediction of stock market movements using sentiment analysis and deep learning.

Stock-Prediction- In this project, we aim to enhance the prediction of stock market movements using sentiment analysis and deep learning. We divide th

5 Jan 25, 2022
[CVPR2021] UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles

UAV-Human Official repository for CVPR2021: UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicle Paper arXiv Res

129 Jan 04, 2023
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
PyTorch Implementation of NCSOFT's FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis

FastPitchFormant - PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Qu

Keon Lee 63 Jan 02, 2023
A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G

Ching-Yao Chuang 427 Dec 13, 2022
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification

Understanding Bayesian Classification This repository hosts the code to reproduce the results presented in the paper On Uncertainty, Tempering, and Da

Sanyam Kapoor 18 Nov 17, 2022
Wind Speed Prediction using LSTMs in PyTorch

Implementation of Deep-Forecast using PyTorch Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting Adapted from original implementation Setu

Onur Kaplan 151 Dec 14, 2022
This is the formal code implementation of the CVPR 2022 paper 'Federated Class Incremental Learning'.

Official Pytorch Implementation for GLFC [CVPR-2022] Federated Class-Incremental Learning This is the official implementation code of our paper "Feder

Race Wang 57 Dec 27, 2022
Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs

Differential Privacy for Heterogeneous Federated Learning : Utility & Privacy tradeoffs In this work, we propose an algorithm DP-SCAFFOLD(-warm), whic

19 Nov 10, 2022