A new GCN model for Point Cloud Analyse

Related tags

Deep Learning VA-GCN
Overview

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

Model Accuracy
PointNet (Official) 89.2
PointNet2 (Official) 91.9
PointNet2_SSG (Pytorch without normal) 92.2
PointNet2_SSG (Pytorch with normal) 92.4
PointNet2_MSG (Pytorch with normal) 92.8
VA-GCN (Pytorch with normal) 93.5
VA-GCN (Pytorch with normal)+MSI 94.3

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

Model Inctance avg IoU Class avg IoU
PointNet (Official) 83.7 80.4
PointNet2 (Official) 85.1 81.9
PointNet2 (Official) 85.5 82.6

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

Model Class avg IoU
PointNet (Pytorch) 43.7
PointNet2_ssg (Pytorch) 53.5
VA-GCN (Pytorch) 56.9
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