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Part Segmentation on ShapeNet

Data

We use the ShapeNetPart dataset (xyz, normals and labels) from here, which is prepared by PointNet++. Download the dataset and place it to data/shapenetcore_partanno_segmentation_benchmark_v0_normal/.

You can specific your own data directory by changing the dataset augment in train.py.

Training

The settings are similar as in our classification experiment. To train a model on this dataset (require 2 gpus for 2048 points input):

python train.py --gpu_idx 0 1

The models are saved every 10 epochs.

Testing

To evaluate a model from models/train/checkpoints/:

python test.py --log train --checkpoint epoch_099.pkl

Also, you can save the predicted obj files (ground truth, prediction, difference):

python test.py --log train --checkpoint epoch_099.pkl --output ./results