Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction

This repository provides the source code for the paper Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction published in AAAI-22. The implementation is on ShapeNet.

Cite this work

@inproceedings{padmix,
  title={Pose Adaptive Dual Mixup for Few-Shot Single-View 3D Reconstruction},
  author={Cheng, Ta-Ying and 
          Yang, Hsuan-Ru and 
          Trigoni, Niki and 
          Chen, Hwann-Tzong and 
          Liu, Tyng-Luh},
  booktitle={AAAI},
  year={2022}
}

Prerequisites

Clone the Code Repository

git clone https://github.com/ttchengab/PADMix.git

Datasets

The ShapeNet dataset is available below:

• ShapeNet rendering images: http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz
• ShapeNet voxelized models: http://cvgl.stanford.edu/data2/ShapeNetVox32.tgz

Get Started

Create two directories, one for saving templates, and the other for saving checkpoints:

mkdir template
mkdir ckpts_fewshot

To generate the priors, use the following command:

python saveTV.py

To train the ground truth autoencoder, use the following command:

python fewshot_AE.py

To train with Input Mixup, use the following command:

python fewshot_mixup_triplet.py

To train with Latent Mixup, use the following command:

python fewshot_latent_mixup.py

To evaluate the IoU, use the following command:

python fewshot_eval.py

Pretrained Models

The pretrained model under the one-shot setting on ShapeNet is available here.