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

Overview

Dependencies

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

• TensorFlow 1.4
• 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++, PointConv, SGPN and DiscLoss-tf