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A Pytorch implementation of the PointNet netowrk
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README.md

Pytorch PointNet

A Pytorch implementation of the PointNet network.

Reference: "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation", Qi et al

Getting started

Setting up

Install the dependencies using Conda:

conda create --name pytorch_pointnet --file spec-file.txt

Available datasets

  • shapenet: The dataset can be downloaded here. For this dataset, classification and segmentation (part segmentation) tasks are available.
  • mnist: If not found, the dataset will be downloaded automatically. Only the classification task is avialable.

Training

Use the following script for training

python train.py dataset dataset_folder task output_folder 
   --number_of_points 2500
   --batch_size 32
   --epochs 50
   --learning_rate 0.001
   --number_of_workers 4
   --model_checkpoint

where:

  • dataset: is one of the available datasets (e.g. shapenet)
  • dataset_folder: is the path to the root dataset folder
  • task: is either classification or segmentation
  • output_folder: is the output_folder path where the training logs and model checkpoints will be stored
  • number_of_points: is the amount of points per cloud
  • batch_size: is the batch size
  • epochs: is the number of training epochs
  • learning_rate: is the optimizer learning rate
  • number_of_workers: is the number of workers used by the data loader
  • model_checkpoint: is the path to a checkpoint that is loaded before the training begins.

Infer

Use the following script for inference:

python infer.py dataset model_checkpoint point_cloud_file task 

where:

  • dataset: is one of the available datasets (e.g. shapenet)
  • model_checkpoint: is the path to a checkpoint that is loaded before the inference begins.
  • point_cloud_file: is the path to the point cloud file to run the inference on.
  • task: is either classification or segmentation

This will also output a 3d visualization of the point cloud.

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