pointnet-keras

Original tensorflow implementation: https://github.com/charlesq34/pointnet

Package requirement: Python3.6, keras, tensorflow, numpy, matplotlib, h5py

Results

Segmentation Sample

How to Run code:

Classification:

• Download the aligned dataset from Link
• Put all traning .h5 files under Prepdata folder, all testing .h5 files under Prepdata_test folder
• Run train_cls.py. Accuracy rate will be 82.5%, which is slightly lower than the original implementation.

Segmentation:

• Download and unzip the shapenet dataset from Link.
• Run Seg_dataprep.py then train_seg.py.

Point Architecture

• Input Transformation Net: Input: Nx3 point cloud sample, Output: 3x3 transformation net

• Feature Transformation Net:

• Global Feature: Input: Nx3 point cloud sample multiply input T_net. Output: 1*1024 global feature

• Classification Net: Input: Nx3 point cloud sample multiply input T_net. Output: 1x40 softmax prediction