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PointNet in PyTorch

This is a PyTorch re-implementation of PointNet according to the specifications laid out in the paper with two minor differences:

  • I exclude the adaptive batch normalization decay rate
  • The trained model provided operates on pointclouds with 2000 points as opposed to 2048 (although you can re-train and change the pointcloud sizes)

Other Implementations

  • The official TensorFlow implementation from the authors can be found here.
  • Another PyTorch re-implementation can be found here.

If you use my re-implementation for your own work, please cite the original paper:

Qi, Charles R., et al. "Pointnet: Deep learning on point sets for 3d classification and segmentation." 
Proc. Computer Vision and Pattern Recognition (CVPR), IEEE 1.2 (2017): 4.

Repo TO-DO's

  • Finish segmentation implementation
  • Upload the sampled ModelNet40 data
  • Write up how-to section

Classification Results

The pre-trained classifier model included in this repository was trained for 60 epochs with a batch size of 32 on a 2000-point-per-model sampling of ModelNet40.

Here is an graph showing the training loss over 60 epochs:

classifier_training_loss

Below are the accuracy results for the included classifier model on the test set

Overall Accuracy
0.852917
Dresser Chair Piano Keyboard Tent Wardrobe Bookshelf Bed
0.76 0.95 0.83 0.90 1.00 0.65 0.95 0.92
XBox Vase Table Flower Pot Cup Glass Box Night Stand Sink
0.70 0.81 0.70 0.00 0.45 0.89 0.66 0.65
Laptop Airplane Curtain Range Hood Stairs Door Radio Bowl
0.95 0.99 0.80 0.91 0.65 0.85 0.70 1.00
Toilet Plant Monitor Lamp Mantle TV Stand Car Cone
0.88 0.89 0.94 0.75 0.89 0.79 0.91 0.85
Bathtub Bottle Person Stool Bench Guitar Sofa Desk
0.82 0.96 0.85 0.60 0.85 0.91 0.97 0.80