This repo is a PyTorch implementation for Paper "Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds"

Last update: Dec 9, 2022

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Overview

Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds

This repository is a PyTorch implementation for paper: Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds.
Kaizhi Yang, Xuejin Chen
SIGGRAPH 2021

Introduction

Representing complex 3D objects as simple geometric primitives, known as shape abstraction, is important for geometric modeling, structural analysis, and shape synthesis. In this paper, we propose an unsupervised shape abstraction method to map a point cloud into a compact cuboid representation. We jointly predict cuboid allocation as part segmentation and cuboid shapes and enforce the consistency between the segmentation and shape abstraction for self-learning. For the cuboid abstraction task, we transform the input point cloud into a set of parametric cuboids using a variational auto-encoder network. The segmentation network allocates each point into a cuboid considering the point-cuboid affinity. In addition, several novel losses are designed to jointly supervise the two branches in terms of geometric similarity and cuboid compactness.

Dependencies

Python 3.8.8.
CUDA 10.2.
PyTorch 1.5.1.
TensorboardX for visualization of the training process.

Dataset

We provide the ready-to-use datasets:

Dataset

Please unzip this file and set its path as the argument E_shapenet4096.

Training

python E_train.py --E_shapenet4096 PATH_TO_SHAPENET4096 --E_ckpts_folder PATH_TO_SAVE --D_datatype DATA_TYPE

Inference

python E_infer.py --E_shapenet4096 PATH_TO_SHAPENET4096 --E_ckpt_path DIRECTORY_TO_CHECKPOINT --checkpoint CHECKPOINT_NAME

Cite

Please cite our work if you find it useful:

@misc{yang2021unsupervised,
    title={Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds},
    author={Kaizhi Yang and Xuejin Chen},
    year={2021},
    eprint={2106.03437},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

License

MIT License

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Comments

Some issues about cuboid compactness loss

Hi, Kai Zhi, great job. And I have a question about compactness loss after reading the paper. M is a hyperparameter and fixed, why should we use a loss function to reduce the number of parts? Thanks again, looking forward to your reply.

opened by duzhenjiang113 1
Link of 'Dataset' is not shareable, Need Access

Hi, @SilenKZYoung Thanks for the great work. I am interested to try it and explorer more on this paper, but the link of Dataset is not shareable, which needs access and I have requested(yj444757397@gmail).

Thanks & Regards, Jie

opened by tommaoer 1
Releasing pre-trained models?

Hi -- awesome work!

I was just wondering if you were planning on releasing pre-trained models for any categories from ShapeNet? If you are, do you have any idea on when you might do so?

Thanks!

opened by rkjones4 1
Confusion about the Table1 "Quantitative comparison of shape reconstruction performance by Chamfer Distance"

Hello, I encountered a little problem when trying to reproduce the reconstruction performance indicators in your paper. Since there is no such part in the project code, I ask a question here and hope to get your reply. Thank you very much! Based on the pre-trained model, I uniformly sampled the generated geometry according to the method of the paper to obtain a predicted point cloud of 4096 points, and calculate the Chamfer Distance between it and the original point cloud (I used the pcu library to Calculation,link.) The result obtained is roughly an order of magnitude away from yours (0.04 (in the paper) vs 0.004 (my experiment result)). I Note that the point cloud is scale-transformed，as mentioned in the paper, "All shapes are pre-aligned and normalized to the unit scale", I am not sure if there are some other operations before calculating the CD distance. What should I do to get the CD index in the paper? Thanks very much!

Attachment: The left side of the figure is my result after uniform sampling, and the right side is the original point cloud result. I calculated the CD between these two point clouds.

opened by ifrozenwhale 6

This repo is a PyTorch implementation for Paper "Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds"

Related tags

Overview

Unsupervised Learning for Cuboid Shape Abstraction via Joint Segmentation from Point Clouds

Introduction

Dependencies

Dataset

Training

Inference

Cite

License

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Comments

Some issues about cuboid compactness loss

Link of 'Dataset' is not shareable, Need Access

Releasing pre-trained models?

Confusion about the Table1 "Quantitative comparison of shape reconstruction performance by Chamfer Distance"

Owner

Kaizhi Yang

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