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WXinlong
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Description

Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019

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# 80,036
Python
C++
Shell
pytorch
2 commits

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

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

    bash
    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

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