Awesome

Associatively Segmenting Instances and Semantics in Point Clouds

The full paper is available at: https://arxiv.org/abs/1902.09852. Qualitative results of ASIS on the S3DIS and vKITTI test fold:

Overview

Dependencies

The code has been tested with Python 2.7 on Ubuntu 14.04.

• TensorFlow
• h5py

Data and Model

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

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

• (optional) Trained model can be downloaded from here.

Usage

• Compile TF Operators

  Refer to PointNet++

• Training

cd models/ASIS/
ln -s ../../data .
sh +x train.sh 5

• Evaluation

python eval_iou_accuracy.py

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{wang2019asis,
	title={Associatively Segmenting Instances and Semantics in Point Clouds},
	author={Wang, Xinlong and Liu, Shu and Shen, Xiaoyong and Shen, Chunhua, and Jia, Jiaya},
	booktitle={CVPR},
	year={2019}
}

Acknowledgemets

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