hvkwak / pie-net

PIE-NET: Parametric Inference of Point Cloud Edges

This is the code repository for "PIE-NET: Parametric Inference of Point Cloud Edges” Created by Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, Hao Zhang

Prerequisites:

Numpy (ver. 1.13.3)
TensorFlow (ver. 1.4.1)
scipy (ver. 0.19.1)
Matlab (ver. 2015a) 
Python (ver. 2.7)
Cuda (ver. 8.0)

This repository is based on Tensorflow and the TF operators from PointNet++ and PointNet. Therefore, you need to compile PointNet++ (here） and PointNet (here). The code is tested under TensorFlow 1.4.1 and Python 2.7 on Ubuntu 16.04.

Introduction

There are seven versions of our approach, including a main version (8096 points Input), and six versions of the stress test, include: Noise (+0.01, +0.02, +0.05) and Sparse (1024, 2048, 4096) .

In each version, we provided pre-trained models ('.\main\stage_1_log...') and test data ('.\main\test_data...').

In the main version, the test data we provided included two parts : 1, ABC dataset ('.\model\test_data\101.mat', '102.mat') with GT. 2, Novel Categories ('.\model\test_data\135.mat', '136.mat', '137.mat') without GT.

In other versions, we only provided the ABC Dataset as test data. Also, in the main version, we provide training samples ('.\model\train_data'), if you want to retrain the model ('Python train_stage_12.py --stage=1').

We provide the pre-trained model in folder ('.\main\stage_1_log...'). To evaluate the model, you need to put the test point clouds ('.mat' format) in the folder '.\main\test_data...'

Train：

to train edge and corner points detection    
cd main
python train_stage_12.py --stage=1

Test：

to test edge and corner points detection    
cd main
python test_stage_12.py --stage=1

Train_stage2 (./Stage2/main/)

python train_stage_2.py --stage=1

Test_stage2 (./Stage2/main/)

python test_stage_2.py --stage=1

Visualization:

visualization.m: This file is used to visualize the detection results.
Vis_closed_final.m (./stage2/main/): this file is used for closed curve detection post-precessing
Vis_open_final.m (./stage2/main/): this file is used for open curve detection post-precessing

Comparison with EC-Net

The following is the complete process of generating training samples（EC-NET）using ABC data (see Figure 4 in EC-NET paper):

1, Extract the mesh information from the existing file（see Figure 4 (a)）

1.1, Extract all 'vertices', 'faces' from '.obj' file

1.2, Extract all 'curves' from '.yml' file Fields:
sharp: true;
type: Bspline/line/Cycle; vert_indices: Contains all the vertex indexes that belong to the curve Note that in our work, we only consider sharp curve ('sharp: true'). A non-sharp curve ('sharp: false') provided in the '.yml' file, which is not in our consideration.

1.3, For EC-NET annotated edges （see Figure 4 (b)） 'faces': N_f9; each facet is represented by three vertices 'vertices'：N_v3; （x,y,z） 'curves' : N_c*6; each 'curve' is made up of a large number of small line segments, each represented by two 'vertices'

1.4, point cloud sampling （see Figure 4 (c)）

1.4.1，'Dense_sample_points' : According to 'Vertices' and 'Faces' (based on the area of the triangular face) , 100,000 points are sampled for each model; 1.4.2, 'Sparse_sample_points' : Using the Farthest Point Sampling (FPS) algorithm to sample 8096 points from 100,000 points.

1.5, generate EC-NET training data （see Figure 4 (d) (e)）

Finally, using the above entries ('faces', 'vertices', 'curves', 'Sparse_sample_points') as input, you can generate the training data using the '../code/prepare_data.py' file provided by EC-Net.