Surface Reconstruction from Point Clouds by Learning Predictive Context Priors (CVPR 2022)
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This repository contains the code to reproduce the results from the paper. [Surface Reconstruction from Point Clouds by Learning Predictive Context Priors](https://arxiv.org/abs/2204.11015).You can find detailed usage instructions for training your own models and using pretrained models below.
If you find our code or paper useful, please consider citing
@inproceedings{PredictiveContextPriors,
title = {Surface Reconstruction from Point Clouds by Learning Predictive Context Priors},
author = {Baorui, Ma and Yu-Shen, Liu and Matthias, Zwicker and Zhizhong, Han},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
Surface Reconstruction Demo
Predicted Queries Visualization
Predicted queries in Loccal Coorinate System
Installation
First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda.
You can create an anaconda environment called tf using
conda env create -f tf.yaml
conda activate tf
Training
You should train the Local Context Prior Network first, run
python pcp.py --input_ply_file test.ply --data_dir ./data/ --CUDA 0 --OUTPUT_DIR_LOCAL ./local_net/ --OUTPUT_DIR_GLOBAL ./glocal_net/ --train --save_idx -1
You should put the point cloud file(--input_ply_file, only ply format) into the '--data_dir' folder.
Then train the Predictive Context Prior Network, run
python pcp.py --input_ply_file test.ply --data_dir ./data/ --CUDA 0 --OUTPUT_DIR_LOCAL ./local_net/ --OUTPUT_DIR_GLOBAL ./glocal_net/ --finetune --save_idx -1
Test
You can extract the mesh model from the trained network, run
python pcp.py --input_ply_file test.ply --data_dir ./data/ --CUDA 0 --OUTPUT_DIR_LOCAL ./local_net/ --OUTPUT_DIR_GLOBAL ./glocal_net/ --test --save_idx -1
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