Pytorch Implementation of PointNet and PointNet++

This repo is implementation for VA-GCN in pytorch.

Classification (ModelNet10/40)

Data Preparation

Download alignment ModelNet here and save in data/modelnet40_normal_resampled/.

Run

You can run different modes with following codes.

• If you want to use offline processing of data, you can use --process_data in the first run. You can download pre-processd data here and save it in data/modelnet40_normal_resampled/.
• If you want to train on ModelNet10, you can use --num_category 10.

# ModelNet40
## Select different models in ./models 

## e.g., pointnet2_ssg without normal features
python train_classification.py --model VA-GCN_cls --log_dir VA-GCN_cls
python test_classification.py --log_dir VA-GCN_cls

## e.g., pointnet2_ssg with normal features
python train_classification.py --model VA-GCN_cls --use_normals --log_dir VA-GCN_cls_normal
python test_classification.py --use_normals --log_dir VA-GCN_cls_normal

Performance

Part Segmentation (ShapeNet)

Data Preparation

Download alignment ShapeNet here and save in data/shapenetcore_partanno_segmentation_benchmark_v0_normal/.

Run

## Check model in ./models 
## e.g., pointnet2_msg
python train_partseg.py --model VA-GCN_part_seg --normal --log_dir VA-GCN_part_seg
python test_partseg.py --normal --log_dir VA-GCN_part_seg_normal

Performance

Semantic Segmentation (S3DIS)

Data Preparation

Download 3D indoor parsing dataset (S3DIS) here and save in data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/.

cd data_utils
python collect_indoor3d_data.py

Processed data will save in data/s3dis/stanford_indoor3d/.

Run

## Check model in ./models 
## e.g., pointnet2_ssg
python train_semseg.py --model VA-GCN_sem_seg --test_area 5 --log_dir VA-GCN_sem_seg
python test_semseg.py --log_dir VA-GCN_sem_seg --test_area 5 --visual

Visualization results will save in log/sem_seg/pointnet2_sem_seg/visual/ and you can visualize these .obj file by MeshLab.

Performance