Tianxinhuang / FPN

The codes for Fast Point Cloud Sampling Network published in Pattern Recognition Letter

FPN

The codes for Fast Point Cloud Sampling Network published in Pattern Recognition Letter

Environment

• TensorFlow 1.13.1
• Cuda 10.0
• Python 3.6.9
• numpy 1.14.5

Dataset

The adopted ShapeNet Part dataset is adopted following FoldingNet, while the ModelNet10 and ModelNet40 datasets follow PointNet. Other datasets can also be used. Just revise the path by the (--filepath) parameter when training or evaluating the networks. The organization of training data is the same as PCDNet.

Usage

1. Preparation

cd ./tf_ops
bash compile.sh

The pre-trained reconstruction and recognition networks are conducted following PCDNet. They should be included in the parameter (--prepath).

2. Training

For the reconstruction task,

Python3 pc_sampling_rec.py

For the recognition task,

Python3 pc_sampling_cls.py

Note that the path of data (--filepath) should be edited according to your setting.

3. Evaluation

For the reconstruction task,

Python3 eva_rec.py

For the recognition task,

Python3 eva_cls.py

The trained weight files should be put in (--savepath) to evaluate the sampling performances.