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PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding

Annotation System Overview

Figure 1. The PartNet Annotation System Overview.

Annotation System

This repo contains the web-based part segmentation annotation interface for PartNet.

Our 3D web-based GUI is build upon Node.js, Express.js and Three.js frameworks. Please check the README in client and server folders for setup instructions.

Paper and Dataset

PartNet is accepted to CVPR 2019. See you at Long Beach, CA.

Our team: Kaichun Mo, Shilin Zhu, Angel X. Chang, Li Yi, Subarna Tripathi, Leonidas J. Guibas and Hao Su from Stanford, UCSD, SFU and Intel AI Lab.

Arxiv Version: https://arxiv.org/abs/1812.02713

Project Page: https://cs.stanford.edu/~kaichun/partnet/

Video: https://youtu.be/7pEuoxmb-MI

Please refer to this repo for the PartNet dataset utilities and this repo for the segmentation experiments (Section 5) in the paper.

Citations

@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}
}

License

MIT Licence

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