Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds
Bo Yang, Jianan Wang, Ronald Clark, Qingyong Hu, Sen Wang, Andrew Markham, Niki Trigoni. arXiv:1906.01140, 2019.
(1) Setup
ubuntu 16.04 + cuda 8.0
python 2.7 or 3.6
tensorflow 1.2 or 1.4
scipy 1.3
h5py 2.9
open3d-python 0.3.0
Compile tf_ops
(1) To find tensorflow include path and library paths:
import tensorflow as tf
print(tf.sysconfig.get_include())
print(tf.sysconfig.get_lib())
(2) To change the path in all the complie files, e.g. tf_ops/sampling/tf_sampling_compile.sh, and then compile:
cd tf_ops/sampling
chmod +x tf_sampling_compile.sh
./tf_sampling_compile.sh
(2) Data
S3DIS: https://drive.google.com/open?id=1hOsoOqOWKSZIgAZLu2JmOb_U8zdR04v0
百度盘: https://pan.baidu.com/s/1ww_Fs2D9h7_bA2HfNIa2ig 密码:qpt7
Acknowledgement: we use the same data released by JSIS3D.
(3) Train/test
python main_train.py
python main_eval.py
(4) Quantitative Results on ScanNet
(5) Qualitative Results on ScanNet
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More results of ScanNet validation split are available at: More ScanNet Results
To visualize: python helper_data_scannet.py
(6) Qualitative Results on S3DIS
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(7) Training Curves on S3DIS
(8) Video Demo (Youtube)
Citation
If you find our work useful in your research, please consider citing:
@inproceedings{yang2019learning,
title={Learning object bounding boxes for 3d instance segmentation on point clouds},
author={Yang, Bo and Wang, Jianan and Clark, Ronald and Hu, Qingyong and Wang, Sen and Markham, Andrew and Trigoni, Niki},
booktitle={Advances in Neural Information Processing Systems},
pages={6737--6746},
year={2019}
}
