WXinlong / Asis
Licence: mit
Associatively Segmenting Instances and Semantics in Point Clouds, CVPR 2019
Stars: β 228
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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.
- 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
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