NNU-GISA / BAN

Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds（BAN)

Bi-Directional Attention for Joint Instance and Semantic Segmentation in Point Clouds(BAN)

The full paper is available at: https://arxiv.org/abs/2003.05420

Dependencies

The code has been tested with Python 3.7 on Ubuntu 16.04.

• tensorflow 1.14
• h5py
• IPython
• scipy

Data and Model

Download 3D indoor parsing dataset (S3DIS Dataset). Version 1.2 of the dataset is used in this work.

python collect_indoor3d_data.py
python gen_h5.py
cd data && python generate_input_list.py
cd ..

Usage

• Compile TF Operators
  Refer to PointNet++
• Training

python train.py

• Estimate_mean_ins_size

python estimate_mean_ins_size.py

• Test

python test.py

• Evaluation

python eval_iou_accuracy.py

Acknowledgemets

This code largely benefits from following repositories: PointNet++, SGPN, DGCNN, DiscLoss-tf and ASIS