Awesome
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
This repository contains code and scripts for PartNet segmentation experiments in Section 5.
About this repository
data/
sem_seg_h5/ # the train/val/test data for Sec 5.1
ins_seg_h5/ # an intermediate data format for Sec 5.3
ins_seg_h5_for_detection/ # the train/val data for our proposed method in Sec 5.3
ins_seg_h5_for_sgpn/ # the train/val data for SGPN baseline in Sec 5.3
ins_seg_h5_gt/ # the ground-truth test data in Sec 5.3
exps/
sem_seg_pointcnn # the code for PointCNN baseline in Sec 5.1
ins_seg_detection/ # the code for our proposed method in Sec 5.3
ins_seg_sgpn/ # the code for SGPN baseline in Sec 5.3
utils/ # some utility functions
tf_ops/ # some customized Tensorflow layers (you may need to re-compile them on your machine)
stats/
all_valid_anno_info.txt # Store all valid PartNet Annotation meta-information
# <anno_id, version_id, category, shapenet_model_id, annotator_id>
before_merging_label_ids/ # Store all expert-defined part semantics before merging
Chair.txt
...
merging_hierarchy_mapping/ # Store all merging criterion
Chair.txt
...
after_merging_label_ids/ # Store the part semantics after merging
Chair.txt # all part semantics
Chair-hier.txt # all part semantics that are selected for Sec 5.2 experiments
Chair-level-1.txt # all part semantics that are selected for Sec 5.1 and 5.3 experiments for chair level-1
Chair-level-2.txt # all part semantics that are selected for Sec 5.1 and 5.3 experiments for chair level-2
Chair-level-3.txt # all part semantics that are selected for Sec 5.1 and 5.3 experiments for chair level-3
...
train_val_test_split/ # An attemptive train/val/test splits (may be changed for official v1 release and PartNet challenges)
Chair.train.json
Chair.val.json
Chair.test.json
Dataset Repo
Please check the dataset repo for downloading the dataset and helper scripts for data usage.
Cite
@InProceedings{Mo_2019_CVPR,
author = {Mo, Kaichun and Zhu, Shilin and Chang, Angel X. and Yi, Li and Tripathi, Subarna and Guibas, Leonidas J. and Su, Hao},
title = {{PartNet}: A Large-Scale Benchmark for Fine-Grained and Hierarchical Part-Level {3D} Object Understanding},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}
Questions
Please post issues for questions and more helps on this Github repo page. For data annotation error, please fill in this errata.
License
MIT Licence