• PCN: Point Completion Network.
• • Algorithm analysis
  • Running commands
  • Quantitative Result Comparison
  • • Seen Categories:
    • Seen Categories:
  • Contributors

PCN: Point Completion Network.

PCN is a point cloud completion algorithm based on deep learning, which is collected into our open source algorithm library of point cloud. The original project is run on python-3 and tensorflow-1.12 with CUDA 9.0 and tested on Ubuntu 16.04 with Python 3.5, which is in folder './PCN-master'. We add the PyTorch version which is in folder './PCN-PyTorch'.

Algorithm analysis

This project designes a deep learning architecture which combines advantages from existing architectures to generate a dense point cloud in a coarse-to-fine fashion, enabling high resolution completion with much fewer parameters than voxelbased models.

Running commands

Tensorflow

1. Install environment

pip install -r requirments.txt
pip install open3d

2. Train
   Download training (train.lmdb, train.lmdb-lock) and validation (valid.lmdb, valid.lmdb-lock) data from shapenet or shapenet_car directory on Google Drive

python3 train.py

3. Test

python3 test_shapenet.py

Note: you should revise codes in test_shapenet.py to choose test.list or test_novel.list

PyTorch

1. Built environment on Python-3.6, PyTorch-1.7, and CUDA10.1

2. Compile for cd and emd

cd extensions/chamfer_distance
python setup.py install
cd ../earth_movers_distance
python setup.py install

3. Training

python train.py --exp_name PCN_16384 --lr 0.0001 --epochs 400 --batch_size 32 --coarse_loss cd --num_workers 8

4. Test

python test.py --exp_name PCN_16384 --ckpt_path checkpoint/best_l1_cd.pth --batch_size 32 --num_workers 8

Quantitative Result Comparison

We compare the algorithm performance of different frameworks under tensorflow and PyTorch respectively. The main evaluation metric is CD. Note: we remove emd and F-score because of some bugs.

Tensorflow:

Seen Categories:

Category	L1_CD(1e-3)	L2_CD(1e-4)
Airplane	5.5025	1.4003
Cabinet	10.6254	4.4503
Car	8.6960	2.4445
Chair	10.9983	4.8382
Lamp	11.3389	6.2384
Sofa	11.6759	5.1293
Table	8.5901	3.5689
Vessel	9.6649	4.0620
Average	9.6365	4.0165

PyTorch:

Seen Categories:

Category	L1_CD(1e-3)	L2_CD(1e-4)
Airplane	6.0028	1.7323
Cabinet	11.2092	4.7351
Car	9.1304	2.7157
Chair	12.0340	5.8717
Lamp	12.6754	7.5891
Sofa	12.8218	6.4572
Table	9.8840	4.5669
Vessel	10.1603	4.2766
Average	10.4897	4.7431

Contributors

name: Zhang Yongchi
email: zhangych02@pcl.ac.cn