GitHub - WXinlong/ASIS: Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019 (original) (raw)

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 andDiscLoss-tf