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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.

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

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Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019

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