This repo demonstrates how to reproduce the results "Rotation-aware-3D-vehicle-detector"

From the SECOND for KITTI object detection

We make minimum changes for implementing our algorithms (Using SECOND as backbone network)

Evaluation is on the KITTI dataset

Getting Started

This is a fork of SECOND for KITTI object detection and the relevant subset of the original README is reproduced here.

Installation

For environment settings, we follow this repo; PointPillars

1. Clone code

git clone https://github.com/hyunjunChhoi/Rotation-aware-3D-vehicle-detector/git

2. Install dependence python packages

Following PointPillars

3. Some additional installation (RRPN Module)

We also refers to the open source

[RRPN]https://github.com/mjq11302010044/RRPN_pytorch

Follow the installation process

Install RRPN module

and then replace it with ./RRPN_module_modified(Some additional modification)

4. add second.pytorch/ to PYTHONPATH

Prepare dataset (following SECOND)

• KITTI Dataset preparation

Download KITTI dataset and create some directories first:

└── KITTI_DATASET_ROOT
       ├── training    <-- 7481 train data
       |   ├── image_2 <-- for visualization
       |   ├── calib
       |   ├── label_2
       |   ├── velodyne
       |   └── velodyne_reduced <-- empty directory
       └── testing     <-- 7580 test data
           ├── image_2 <-- for visualization
           ├── calib
           ├── velodyne
           └── velodyne_reduced <-- empty directory

Then run

python create_data.py kitti_data_prep --data_path=KITTI_DATASET_ROOT

Minor changes from the original code

• /second/pytorch/models/voxelnet -> /second/pytorch/models/voxeltwonet

• some additional files in /second/pytorch/models (ROI head network)

• Import RRPN module (RROI pooling method)

• some modification on train.py and inference.py and config files

Usage

train

1. load pretrained network (original SECOND as backbone, /second/old_voxelnet-61900.tckpt )

2. freeze it and train only ROI_head network

train with single GPU

CUDA_VISIBLE_DEVICES=0 python train.py train --config_path=dir/second/configs/car_two_norotate_ori_no1.fhd.config --model_dir=dir/second/output_voxeltwonet_old_61900_freeze --pretrained_path=dir/second/old_voxelnet-61900.tckpt --pretrained_exclude=ROI_head  --freeze_exclude=ROI_head’

evaluate

CUDA_VISIBLE_DEVICES=0 python train.py evaluate --config_path=dir/second/configs/car_two_norotate_ori_no1.fhd.config --model_dir=dir/second/output_voxeltwonet_old_61900_freeze

Various experiments

modify config files in dir/second/configs/