VoxelNet

Introduction

This is a reproduction of voxelnet based on PyTorch. The original can be referred to link.

Major features

• Pytorch

• Multi GPUs

Updates

Benchmark and model zoo

Installation

Requirements

• Linux (tested on Ubuntu 16.04 )
• Python 3.6
• PyTorch 0.4.0 and torchvision
• Cython
• tensorboard
• mayavi

Install VoxelNet

a. Install PyTorch 0.4.0 and torchvision following the official instructions.

b. Clone the VoxelNet repository.

git clone https://github.com/Yc174/voxelnet.git

c. Compile cuda extensions.

cd lib/extensions/_nms
sh build.sh

Prepare Kitti dataset.

It is recommended to symlink the dataset root to $voxelnet/datasets.

voxelnet
├── lib
├── tools
├── experiments
├── datasets
│   ├── KITTI
│   │   ├── object
│   │   │     ├── training
│   │   │     ├── testing

Train a model

• Multi GPUs training
• use tensorboard to visualize loss
• use validate dataset

To train kitti dataset and save the model.

cd voxelnet
./experiments/run_trainval.sh

Inference with pretrained models

Test a dataset

• single GPU testing
• visualize detection results

To test kitti dataset and visualize the results.

cd voxelnet
./experiments/run_test.sh