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README.md

Iterative Transformer Network for 3D Point Cloud

[paper] [data]

Introduction

This repository contains the implementation of Iterative Transformer Network (IT-Net), a network module that predicts 3D rigid transformations from partial point clouds in an iterative fashion. IT-Net can be used independently for canonical pose estimation or jointly with downstream networks for shape classification and part segmentation. Please refer to our paper for more details.

Citation

If you find our work useful, please consider citing our paper.

@article{yuan2018iterative,
  title   = {Iterative Transformer Network for 3D Point Cloud},
  author  = {Yuan, Wentao and Held, David and Mertz, Christoph and Hebert, Martial},
  journal = {arXiv preprint arXiv:1811.11209},
  year    = {2018}
}

Usage

1) Setup

  1. Install dependencies by running pip install -r requirments.txt.
  2. Download data and pre-trained models from Google Drive.

This code is tested on Ubuntu 16.04 with CUDA 9.0 and python 3.6.

2) Object Pose Estimation

Here is an example command to get results with pretrained model.

python test_pose.py --transformer it_net --n_iter 10 --lmdb data/shapenet_pose/car/test.lmdb --checkpoint data/trained_models/pose_estimation/car-5_iter --results results/pose_estimation/car

This will generate statistics as well as visualizations shown in the paper for car pose estimation. The default setting uses 5 unrolled iterations during training and 10 during testing (controlled by the n_iter option). Besides the car category, we also provide data and pre-trained model for the chair category.

We provide implementations for three different 3D transformer networks, including T-Net (baseline), IT-Net with PointNet backbone (evaluated in the paper) and IT-Net with DGCNN backbone (achieves slightly better results at the cost of more computation and memory). Use the transformer option to switch between different architectures.

3) 3D Shape Classification

Here is an example command to get results with pretrained model.

python test_cls.py --classifier pointnet --transformer it_net --n_iter 2 --lmdb data/partial_modelnet40/test.lmdb --checkpoint data/trained_models/classification/pointnet-2_iter --results results/classification/pointnet

This will evaluate the 2-iteration IT-Net trained jointly with PointNet classifier on the partial ModelNet40 dataset. The classifier option selects the downstream classification network (PointNet or DGCNN). The transformer option selects the 3D transformer model. The n_iter options specifies the number of unrolled iterations for the 3D transformer. We also provide pre-trained 2-iteration IT-Net with DGCNN classifier.

4) Object Part Segmentation

Here is an example command to get results with pretrained model.

python test_seg.py --classifier pointnet --segmenter it_net --n_iter 2 --lmdb data/shapenet_part/test.lmdb --checkpoint data/trained_models/segmentation/pointnet-2_iter --results results/segmentation/pointnet

The options are similar to classification, except that the downstream networks are point segmentation networks (controlled via the segmenter option) and the dataset is ShapeNet Part. We provide two pre-trained models: one with PointNet and one with DGCNN as the segmentation backbone.

5) Data Generation

The prepare_data folder contains scripts to generate partial point clouds from CAD datasets. Feel free to use these scripts to generate your own partial point cloud data.

License

This project code is released under the MIT License.

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Implementation of Iterative Transformer Network for 3D Point Cloud

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