Implementation of PointNet, PointNet++, PointCNN, etc. in PyTorch.

python 3.8.18
torch 2.1.1+cu121

Training was done on Tesla V100-PCIE-16GB gpu. But code does support CPU training.

Download the following datasets and place in data/ directory.

• ModelNet40 from https://modelnet.cs.princeton.edu ModelNet40 Subset.
• ModelNet40_ply_hdf5_2048 from https://shapenet.cs.stanford.edu/media/modelnet40_ply_hdf5_2048.zip
• ShapeNetSubset from https://web.stanford.edu/~ericyi/project_page/part_annotation/index.html, https://shapenet.cs.stanford.edu/ericyi/shapenetcore_partanno_v0.zip (alignment shapenet)
• ModelNet40_Normal_Resampled from https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip, preprocessed https://drive.google.com/drive/folders/1_fBYbDO3XSdRt3DSbEBe41r5l9YpIGWF?usp=sharing
• TU-Berlin from http://cybertron.cg.tu-berlin.de/eitz/projects/classifysketch/sketches_svg.zip
• Cifar10 from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
• Mnist from
  • http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
  • http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
  • http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
  • http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
• Quickdraw
  • https://raw.githubusercontent.com/googlecreativelab/quickdraw-dataset/master/categories.txt
  • https://storage.googleapis.com/quickdraw_dataset/sketchrnn/ + <above categories>.npz
• S3DIS Dataset

Usage of the dataset can be found at vis_modelnet40_normal_resampled.ipynb

Airplane (2048 Points)	Airplane (10000 Points)

The ModelNet40_ply_hdf5_2048 is similar, just in a different format. Usage can be found at vis_modelnet40_data.ipynb.

Usage of the dataset can be found at vis_shapenetcore_partanno_seg_norm.ipynb

Airplane (1024)	Airplane (2466)

Usage of the dataset can be found at vis_tu_berlin.ipynb.

Barn 2D	Barn 3D

Usage of the dataset can be found at vis_mnist.ipynb

Train with following command

python train.py --model pointnet_cls --dataset ModelNet40 --batch_size 32 --epoch 100

Example evaluations can be found at eval_pointnet_cls.ipynb

TODO

Train with the following command

python train.py --model pointcnn_v1_cls --dataset ModelNet40_hdf5 --batch_size 16 --epoch 100

Train with the following command

epochs = 100
batch_size = 32
use_normals = False
use_uniform_sample = False

NOTE: Above PointCN does not yet have data augmentation yet... May explain performance degredation relative to PointNet.

Example Confusion Matrix

PointNet	PointCNN

• PointNet (Pytorch without normal)
• PointNet (Pytorch with normal)
• PointNet2_SSG (Pytorch without normal)
• PointNet2_SSG (Pytorch with normal)
• PointNet2_MSG (Pytorch with normal)
• Compare training/inference performance with those in the paper.

Papers 1.

Other Projects 1.