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.
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
Please check the dataset repo for downloading the dataset and helper scripts for data usage.
@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}
}
Please post issues for questions and more helps on this Github repo page. For data annotation error, please fill in this errata.
MIT Licence