# ASIS

**Repository Path**: Analyst2020/ASIS

## Basic Information

- **Project Name**: ASIS
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-07
- **Last Updated**: 2025-09-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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:

![](misc/s3dis_asis.png)
![](misc/vkitti_asis.png)

## Overview
![](misc/fig.png)

## Dependencies

The code has been tested with Python 2.7 on Ubuntu 14.04.
*  [TensorFlow](https://www.tensorflow.org/)
*  h5py



## Data and Model

* Download 3D indoor parsing dataset ([S3DIS Dataset](https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1)). Version 1.2 of the dataset is used in this work.

``` bash
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](https://drive.google.com/open?id=1UF2nfXdWTOa1iXXmD54_c09rM7pr-kMK).

## Usage

* Compile TF Operators

  Refer to [PointNet++](https://github.com/charlesq34/pointnet2)

* 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++](https://github.com/charlesq34/pointnet2),
[SGPN](https://github.com/laughtervv/SGPN),
[DGCNN](https://github.com/WangYueFt/dgcnn) and
[DiscLoss-tf](https://github.com/hq-jiang/instance-segmentation-with-discriminative-loss-tensorflow)