pointnet

A pytorch implementation of PointNet and PointNet++.

Installation

pip install pointnet

If you encounter No matching distribution found for pointnet using a mirror source, please install from source:

pip install pointnet -i https://pypi.org/simple

Usage

PointNet

Perform classification with inputs xyz coordinates:

import torch
from pointnet import PointNetCls

model = PointNetCls(in_dim=3, out_dim=40)
x = torch.randn(16, 3, 1024)
logits = model(x)

If you have other features, you can put them after the xyz coordinates:

import torch
from pointnet import PointNetCls, STN

in_dim = 3 + 10
stn_3d = STN(in_dim=in_dim, out_nd=3)
model = PointNetCls(in_dim=in_dim, out_dim=40, stn_3d=stn_3d)
xyz = torch.randn(16, 3, 1024)
other_feats = torch.randn(16, 10, 1024)
x = torch.cat([xyz, other_feats], dim=1)
logits = model(x)

Perform semantic segmentation:

import torch
from pointnet import PointNetSeg

model = PointNetSeg(3, 40)
x = torch.randn(16, 3, 1024)
logits = model(x)

PointNet2

Classification:

import torch
from pointnet import PointNet2ClsSSG

model = PointNet2ClsSSG(in_dim=3, out_dim=40)
x = torch.randn(16, 3, 1024)
logits = model(x)

Semantic segmentation:

import torch
from pointnet import PointNet2SegSSG

model = PointNet2SegSSG(in_dim=3, out_dim=10)
x = torch.randn(16, 3, 1024)
xyz = x.clone()
logits = model(x, xyz)

PointNet2 can use taichi to accelerate the computation of ball query. If you are about to train on a single GPU, you can enable taichi by calling enable_taichi().

Perform classification with inputs xyz coordinates:

import torch
from pointnet import PointNet2ClsSSG, enable_taichi

enable_taichi()
model = PointNet2ClsSSG(in_dim=3, out_dim=40).cuda()
x = torch.randn(16, 3, 1024).cuda()
xyz = x.clone()
logits = model(x, xyz)

Performance

Classification accuracy on ModelNet40 dataset (see modelnet40_experiments for details):

Model	input	Overall Accuracy
PointNet (official)	xyz	89.2%
PointNet	xyz	90.7%
PointNet2 (official)	xyz	90.7%
PointNet2SSG	xyz	90.7%
PointNet2MSG	xyz	92.1%

Part segmentation mIoU on ShapeNet dataset (see shapenet_experiments for details):

Model	input	mIoU
PointNet2 (official)	xyz	85.1%
PointNet2SSG	xyz	84.8%
PointNet2MSG	xyz	85.2%

Other Implementationss

charlesq34/pointnet

fxia22/pointnet.pytorch

yanx27/Pointnet_Pointnet2_pytorch

References

@article{qi2017pointnet,
  title={Pointnet: Deep learning on point sets for 3d classification and segmentation},
  author={Qi, Charles R and Su, Hao and Mo, Kaichun and Guibas, Leonidas J},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2017}
}

@article{qi2017pointnet++,
  title={Pointnet++: Deep hierarchical feature learning on point sets in a metric space},
  author={Qi, Charles Ruizhongtai and Yi, Li and Su, Hao and Guibas, Leonidas J},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}