# VoxelNet-pytorch

**Repository Path**: yu_wei_wei/VoxelNet-pytorch

## Basic Information

- **Project Name**: VoxelNet-pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2020-03-26
- **Last Updated**: 2021-06-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Introduction

This is an unofficial implementation of [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://arxiv.org/abs/1711.06396) in pytorch. A large part of this project is based on the work [here](https://github.com/jeasinema/VoxelNet-tensorflow)
# Dependencies
- `python3.5+`
- `pytorch` (tested on 0.3.1)
- `opencv`
- `shapely`
- `mayavi`

# Installation
1. Clone this repository.
2. Compile the Cython module for box_overlaps
```bash
$ python3 setup.py build_ext --inplace
```
3. Compile the nms model
```bash
$ python3 nms/build.py
```


# Data Preparation
1. Download the 3D KITTI detection dataset from [here](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). Data to download include:
    * Velodyne point clouds (29 GB): input data to VoxelNet
    * Training labels of object data set (5 MB): input label to VoxelNet
    * Camera calibration matrices of object data set (16 MB): for visualization of predictions
    * Left color images of object data set (12 GB): for visualization of predictions

2. In this project, the cropped point cloud data for training and validation. Point clouds outside the image coordinates are removed.
```bash
$ python3 data/crop.py
```
3. Split the training set into training and validation set according to the protocol [here](https://xiaozhichen.github.io/files/mv3d/imagesets.tar.gz).
```plain
└── DATA_DIR
       ├── training   <-- training data
       |   ├── image_2
       |   ├── label_2
       |   ├── velodyne
       |   └── crop
       └── testing  <--- testing data
       |   ├── image_2
       |   ├── label_2
       |   ├── velodyne
       |   └── crop
```

# Train




# TODO
- [x] training code
- [x] data augmentation
- [ ] validation code
- [ ] reproduce results for `Car`, `Pedestrian` and `Cyclist`
- [ ] multi-gpu support
- [ ] improve the performances