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Attention-based relation and context modeling for point cloud semantic segmentation (SMI 2020)

Zhiyu Hu, Dongbo Zhang, Shuai Li, Hong Qin

Overview

Environment

Our method is tested in Python 2.7 and TensorFlow 1.4.0 on a workstation with an Intel Core i7-4790 CPU (3.60GHz, 16GB memory) and a GeForce GTX 1070 GPU (8GB memory, CUDA 8.0).

Data

  • Download S3DIS Dataset. Version 1.2 (v1.2_Aligned_Version) 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 ..

Usage

  • Create Conda Environment
  conda create -n ARCM python=2.7
  source activate ARCM
  • Clone the Repository
git clone https://github.com/hu-zhiyu/ARCM && cd ARCM
  • Install Required Dependencies
pip install -r requirements.txt
  • Compile TF Operators
cd tf_ops
sh tf_compile_all.sh

Make sure no errors generated at this step and refer to PointNet++ when encountering any problems.

  • Test with Pre-trained Model
cd models/
python test.py --gpu 0 --log_dir log5_test --model_path log5/epoch_99.ckpt --input_list  meta/area5_data_label.txt --verbose
python eval_iou_accuracy.py

S3DIS Area 5 Quantitative Results

OA mAcc mIoU ceiling floor wall beam column window door table chair sofa bookcase board clutter
88.50 66.09 59.66 93.38 98.45 81.50 0.00 7.00 55.14 48.61 77.16 87.81 50.68 65.54 57.76 52.57

S3DIS Visual Results

Acknowledgements

This code largely benefits from following repositories:

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Attention-based relation and context modeling for point cloud semantic segmentation (SMI 2020)

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