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Multiview Aggregation for Learning Category-Specific Shape Reconstruction, NeurIPS 2019

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# Multiview Aggregation for Learning Category-Specific Shape Reconstruction
## NeurIPS 2019
<a href="http://srinathsridhar.com/" target="_blank">Srinath Sridhar</a>1, <a href="https://davrempe.github.io/" target="_blank">Davis Rempe</a>1, <a href="https://scholar.google.co.uk/citations?user=pZPD0hMAAAAJ&hl=en" target="_blank">Julien Valentin</a>2, <a href="http://sofienbouaziz.com/" target="_blank">Sofien Bouaziz</a>2, <a href="https://geometry.stanford.edu/member/guibas/" target="_blank">Leonidas J. Guibas</a>1,3
 
1<a href="https://www.stanford.edu/" target="_blank">Stanford University</a>, 2<a href="https://vr.google.com/daydream/" target="_blank">Google Inc.</a>, 3<a href="https://research.fb.com/category/facebook-ai-research/" target="_blank">Facebook AI Research</a>

![NOCS Teaser](https://raw.githubusercontent.com/drsrinathsridhar/xnocs/master/xnocs.png)

# About
This repository contains the code and datasets for the NeurIPS 2019 paper **Multiview Aggregation for Learning Category-Specific Shape Reconstruction**. If you use the code or datasets please cite our paper.

```latex
@InProceedings{xnocs_sridhar2019,
author = {Sridhar, Srinath and Rempe, Davis and Valentin, Julien and Bouaziz, Sofien and Guibas, Leonidas J.},
title = {Multiview Aggregation for Learning Category-Specific Shape Reconstruction},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2019}
}
```

# Code
## 1. Download and install the following required packages. We only support Python 3.5+.

- PyTorch 1.0+ (see the [PyTorch website][1] for installation instructions)
- numpy>=1.15.4
- opencv-python<=3.2.0.8
- matplotlib
- palettable (`pip install palettable`)
- [tk3dv][2] (`pip install git+https://github.com/drsrinathsridhar/tk3dv.git`)

## 2. Download the datasets (see below for details)

Note: The training code can automatically download the ShapeNetCOCO dataset. **Please see the dataset [readme][3] for more details.**

- [shapenetcoco_v1.zip (172 GB)][6]
- [shapenetplain_v1.zip (5 GB)][5]

## 3. Download the trained weights

These are weights trained on the ShapeNetCOCO dataset and reported in Table 2 in the paper.

- [Single View Network (1 GB)][7]
- [Multi-view Network (upto 5 views) with Permutation Equivariant layers (1 GB)][8]

## 4. Sample code

The main training and evaluation code is `noxray/nxm/nxm.py` for the single-view network and `noxray/mv_nxm.py`

To train the single-view network on the cars category in ShapeNetCOCO, use this command:

```
python nxm.py --mode train --input-dir <BASE DATASET DIR> --output-dir <BASE OUTPUT DIR> --expt-name XNOCS_SV --category cars --arch SegNetSkip --seed 0 --data-limit <SKIP THIS TO USE FULL DATA> --val-data-limit <SKIP THIS TO USE FULL DATA>
```

You can also load the above config from a file using the `@` seperator. Note: in the paper we use `--learning-rate 0.0001` and `--batch-size 1`. The code is not well-tested with larger batch sizes and there have been reported issues with doing so. For reference, when using the ShapeNetPlain dataset in the paper we train for `--epochs 100` which takes just over 1 day for both the Cars and Airplanes categories (NVIDIA V100 GPU). Training on ShapeNetCOCO is considerably slower.

To evaluate the network and write the output images, use this command:

```
python nxm.py --mode val --input-dir <BASE DATASET DIR> --output-dir <BASE OUTPUT DIR> --expt-name XNOCS_SV --category cars --arch SegNetSkip --seed 0 --test-samples 1000000
```

Note: the batch size is currently hard-coded to 1 for evaluation.

The multi-view network commands are very similar to the above.

# Datasets

We provide two datasets: ShapeNetCOCO and ShapeNetPlain. ShapeNetCOCO is the primary dataset of which we demonstrate reconstruction results. ShapeNetPlain is the dataset we use for comparison with 3D-R2N2 and DPC.

- [shapenetcoco_v1.zip (172 GB)][6]
- [shapenetplain_v1.zip (5 GB)][5]

**Please see the dataset [readme][3] for more details.**

# Contact

Srinath Sridhar ([ssrinath@cs.stanford.edu][4])

[1]: https://pytorch.org/
[2]: https://github.com/drsrinathsridhar/tk3dv
[3]: dataset/README.md
[4]: mailto:ssrinath@cs.stanford.edu
[5]: http://download.cs.stanford.edu/orion/xnocs/shapenetplain_v1.zip
[6]: http://download.cs.stanford.edu/orion/xnocs/shapenetcoco_v1.zip
[7]: http://download.cs.stanford.edu/orion/xnocs/single_view_shapenet_coco_weights.zip
[8]: http://download.cs.stanford.edu/orion/xnocs/multi_view_5views_variable_shapenet_coco_weights.zip

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