JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds

Overview

Dependencies

The code has been tested with Python 3.5 on Ubuntu 16.04.

TensorFlow
h5py

Data and Model

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

python utils/s3dis_utils/collect_indoor3d_data.py
python utils/s3dis_utils/s3dis_gen_h5.py
cd data && python generate_input_list.py && python generate_train_test_list.py
cd ..

(optional) Prepared HDF5 data for training is available here.
(optional) Trained model can be downloaded from here.

Usage

Compile TF Operators

Refer to PointNet++
Training, Test, and Evaluation

cd models/JISS/
ln -s ../../data .

# training
python train.py \
--gpu 0 \
--data_root ./ \
--data_type numpy \
--max_epoch  100  \
--log_dir ../../logs/train_5 \
--input_list data/train_file_list_woArea5.txt

# estimate_mean_ins_size 
python estimate_mean_ins_size.py \
--data_root ./ \
--input_list data/train_hdf5_file_list_woArea5.txt \
--out_dir ../../logs/train_5

# test
python test.py \
--gpu 0 \
--data_root ./ \
--data_type hdf5 \
--bandwidth 0.6   \
--num_point 4096  \
--log_dir ../../logs/test_5 \
--model_path ../../logs/train_5/epoch_99.ckpt \
--input_list  data/test_hdf5_file_list_Area5.txt

# evaluation
python eval_iou_accuracy.py --log_dir ../../logs/test_5

Note: We test on Area5 and train on the rest folds in default. 6 fold CV can be conducted in a similar way.

Citation

If our work is useful for your research, please consider citing:

@inproceedings{zhao2020jsnet,
	title={JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds},
	author={Zhao, Lin and Tao, Wenbing},
	booktitle={Thirty-Fourth AAAI Conference on Artificial Intelligence},
	year={2020}
}

Acknowledgemets

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