# MVCNN-PyTorch

**Repository Path**: cvlab/MVCNN-PyTorch

## Basic Information

- **Project Name**: MVCNN-PyTorch
- **Description**: Multi-View CNN built on ResNet/AlexNet to classify 3D objects
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-10-27
- **Last Updated**: 2025-10-27

## Categories & Tags

**Categories**: machine-learning

**Tags**: None

## README

# MVCNN-PyTorch
## Multi-View CNN built on ResNet/AlexNet to classify 3D objects
A PyTorch implementation of MVCNN using ResNet, inspired by the paper by [Hang Su](http://vis-www.cs.umass.edu/mvcnn/docs/su15mvcnn.pdf).
MVCNN uses multiple 2D images of 3D objects to classify them. You can use the provided dataset or create your own.

Also check out my [RotationNet](https://github.com/RBirkeland/RotationNet) implementation whitch outperforms MVCNN (Under construction).

![MVCNN](https://preview.ibb.co/eKcJHy/687474703a2f2f7669732d7777772e63732e756d6173732e6564752f6d76636e6e2f696d616765732f6d76636e6e2e706e67.png)

### Dependencies
* torch
* torchvision
* numpy
* tensorflow (for logging)

### Dataset
ModelNet40 12-view PNG dataset can be downloaded from [Google Drive](https://drive.google.com/file/d/0B4v2jR3WsindMUE3N2xiLVpyLW8/view).

You can also create your own 2D dataset from 3D objects (.obj, .stl, and .off), using [BlenderPhong](https://github.com/WeiTang114/BlenderPhong)

### Setup
```bash
mkdir checkpoint
mkdir logs
```

### Train
To start training, simply point to the path of the downloaded dataset. All the other settings are optional.

```
python controller.py <path to dataset>  [--depth N] [--model MODEL] [--epochs N] [-b N]
                                        [--lr LR] [--momentum M] [--lr-decay-freq W]
                                        [--lr-decay W] [--print-freq N] [-r PATH] [--pretrained]
```

To resume from a checkpoint, use the -r tag together with the path to the checkpoint file.

### Tensorboard
To view training logs
```
tensorboard --logdir='logs' --port=6006
```