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.

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Repo TO-DO's

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

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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:

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