uprightnet-cvpr2022
Upright-Net: Learning Upright Orientation for 3D Point Cloud (CVPR2022) Code and Data
Science Score: 31.0%
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○Scientific vocabulary similarity
Low similarity (8.2%) to scientific vocabulary
Repository
Upright-Net: Learning Upright Orientation for 3D Point Cloud (CVPR2022) Code and Data
Basic Info
- Host: GitHub
- Owner: XufangPANG
- License: gpl-3.0
- Language: MATLAB
- Default Branch: main
- Size: 43.2 MB
Statistics
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
UprightNet-CVPR2022
Upright-Net: Learning Upright Orientation for 3D Point Cloud (CVPR2022) Code and Data
This repo contains the dataset and model for the paper Upright-Net: Learning Upright Orientation for 3D Point Cloud by Xufang PANG Feng LI et al.
- The source code has been released on Gitlab, see the link (https://gitlab.com/open-source7452523/uprightnet)
Dataset - UprightNet15
We use 15 classes in dataset ModelNet40, which includes 1110 training 3D models and 370 testing models. shapename.txt includes the class name of 15 categories.
pointstrain.npy / pointstest.npy are original data used in our project, with the size of 111020483 for training models and 37020483 for testing models, each 3D model includes 2048 three-dimensional points.
labelstrain.npy / labelstest.npy are corresponding labels for training and testing datasets with data sizes of 11101 and 3701, which are encoded with values between 0 and 14 mapping to the 15 class names listed in shapename.txt, for example, 0 = 'bed'.
pidtrain.npy / pidtest.npy are binary labels for supporting points on 3D models, with the size of 11102048/3702048。
angle56.txt includes 56 points uniformly distributed points sampled on a unit half sphere. By flipping the sign of sampled points, 112 uniformly sampled points on a unit sphere can be obtained.
Code
The folder utils include code that is used for reading .pny files in MATLAB, which you can download from link https://github.com/kwikteam/npy-matlab
rotate.m This file provides code to rotate point clouds and generates its corresponding rotation matrix, for example, input file pointstest.npy, and output rotationtest.npy where each model is rotated 100 times to gain rotated point cloud coordinates (3700020483) and rotm_test.npy with their corresponding transformation matrix.
noise.m This file provides code for adding gaussian noise with parameters mu and sigma, for example, input rotationtest.npy, it output rotationtest_noise.npy, with the size of 3700020483.
Owner
- Login: XufangPANG
- Kind: user
- Repositories: 1
- Profile: https://github.com/XufangPANG
Citation (citation)
X. Pang, F. Li, N. Ding and X. Zhong, "Upright-Net: Learning Upright Orientation for 3D Point Cloud," 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 2022, pp. 14891-14899, doi: 10.1109/CVPR52688.2022.01449.
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@INPROCEEDINGS{9878573,
author={Pang, Xufang and Li, Feng and Ding, Ning and Zhong, Xiaopin},
booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
title={Upright-Net: Learning Upright Orientation for 3D Point Cloud},
year={2022},
volume={},
number={},
pages={14891-14899},
doi={10.1109/CVPR52688.2022.01449}}
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