gujiaqivadin / Neighbor-Vote

Neighbor-Vote

This is the official implementation of Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting, built on OpenPCDet.

@article{chu2021neighbor,
title={Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting},
author={Chu, Xiaomeng and Deng, Jiajun and Li, Yao and Yuan, Zhenxun and Zhang, Yanyong and Ji, Jianmin and Zhang, Yu},
journal={arXiv preprint arXiv:2107.02493},
year={2021}
}

Installation

1. Prepare for the running environment.

   You can follow the installation steps in OpenPCDet.

2. Prepare for the data.

   Please download the official KITTI 3D object detection dataset and you need to prepare your depth maps and put them to data/kitti/training/dorn. To provide ease of use, PatchNet provides the estimated depth maps generated from the pretrained models DORN. And you can directly download the results of 2D detector FCOS on the KITTI train set from here. Please organize the downloaded files as follows:

   Neighbor-Vote
   ├── data
   │   ├── kitti
   │   │   │── ImageSets
   │   │   │── training
   │   │   │   ├──calib & velodyne & label_2 & image_2 & dorn & 2d_score_fcos
   ├── pcdet
   ├── tools
   

   Generate the data infos by running the following command:

   python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml
   

3. Setup.

   python setup.py develop
   

Pretrained Models

The pretrained model can be downloaded from here

Getting Started

Validate the model.

The configuration file is pointpillar.yaml in tools/cfgs/kitti_models, and the validation scripts is in tools/scripts.

cd tools
sh scripts/dist_test.sh ${NUM_GPUS} \
--cfg_file ${CONFIG_FILE} --batch_size ${BATCH_SIZE} --ckpt ${CKPT}

Acknowledge

Thanks to the strong and flexible OpenPCDet codebase maintained by Shaoshuai Shi (@sshaoshuai) and Chaoxu Guo (@Gus-Guo).

Contact

This repository is implemented by Xiaomeng Chu ([e-mail]).