(ICCV2023) MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection

Introduction

This is the implementation of MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection, In ICCV'23, Junkai Xu, Liang Peng, Haoran Cheng, Hao Li, Wei Qian, Ke Li, Wenxiao Wang and Deng Cai. Also a simple way to get 3D geometry and occupancy from monocular images.

Framework

[Paper] [Supp]

News

[2023-8-21] Paper is released on arxiv!
[2023-8-1] Code is released.
[2023-7-14] MonoNeRD is accepted at ICCV 2023!! Code is comming soon.
[2023-2-20] Demo release.
Demo

KITTI Demo

3d-visualization

Installation

Requirements

All the codes for training and evaluation are tested in the following environment:

Linux (tested on Ubuntu 18.04)
Python 3.8
PyTorch 1.8.1
Torchvision 0.9.1
CUDA 11.1.1
spconv 1.2.1 (commit f22dd9)

Installation Steps

a. Clone this repository.

git clone https://github.com/cskkxjk/MonoNeRD.git

b. Install the dependent libraries as follows:

Install the dependent python libraries:
```
pip install -r requirements.txt 
```
Install the SparseConv library, we use the implementation from [spconv].

git clone https://github.com/traveller59/spconv
git reset --hard f22dd9
git submodule update --recursive
python setup.py bdist_wheel
pip install ./dist/spconv-1.2.1-cp38-cp38m-linux_x86_64.whl

Install modified mmdetection from [mmdetection_kitti]

git clone https://github.com/xy-guo/mmdetection_kitti
python setup.py develop

c. Install this library by running the following command:

python setup.py develop

Getting Started

Dataset Preparation

For KITTI, dataset configs are located within configs/stereo/dataset_configs, and the model configs are located within configs/stereo/kitti_models.

For Waymo, dataset configs are located within configs/mono/dataset_configs, and the model configs are located within configs/mono/waymo_models.

Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes are provided by OpenPCDet [road plane], which are optional for training LiDAR models):

Our implementation used a KITTI formated version of Waymo Open Dataset just like [CaDDN], please refer to [Waymo-KITTI Converter] for details.

MonoNeRD_PATH
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & image_3
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
│   ├── waymo
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 ...
│   │   │── validation
│   │   │   ├──calib & velodyne & label_2 & image_2 ...
├── configs
├── mononerd
├── tools

You can also choose to link your KITTI and Waymo dataset path by


YOUR_KITTI_DATA_PATH=~/data/kitti_object
ln -s $YOUR_KITTI_DATA_PATH/ImageSets/ ./data/kitti/
ln -s $YOUR_KITTI_DATA_PATH/training/ ./data/kitti/
ln -s $YOUR_KITTI_DATA_PATH/testing/ ./data/kitti/

YOUR_WAYMO_DATA_PATH=~/data/waymo ln -s YOUR_WAYMO_DATA_PATH/ImageSets/ ./data/waymo/ ln -s YOUR_WAYMO_DATA_PATH/training/ ./data/waymo/ ln -s YOUR_WAYMO_DATA_PATH/validation/ ./data/waymo/


* Generate the data infos by running the following command: 
```python 
python -m mononerd.datasets.kitti.lidar_kitti_dataset create_kitti_infos
python -m mononerd.datasets.kitti.lidar_kitti_dataset create_gt_database_only

python -m mononerd.datasets.waymo.lidar_waymo_dataset creat_waymo_infos
python -m mononerd.datasets.waymo.lidar_waymo_dataset create_gt_database_only

Training & Testing

Train a model

Train with multiple GPUs

./scripts/dist_train.sh ${NUM_GPUS} 'exp_name' ./configs/stereo/kitti_models/mononerd.yaml

Test and evaluate the pretrained models

To test with multiple GPUs:

./scripts/dist_test_ckpt.sh ${NUM_GPUS} ./configs/stereo/kitti_models/mononerd.yaml ./ckpt/pretrained_mononerd.pth

Pretrained Models

KITTI 3D Object Detection Baselines

The results are the BEV / 3D detection performance of Car class on the val set of KITTI dataset.

All models are trained with 4 NVIDIA 3080Ti GPUs and are available for download.
The training time is measured with 4 NVIDIA 3080Ti GPUs and PyTorch 1.8.1.

Training Time	Easy@R40	Moderate@R40	Hard@R40	download
mononerd	~13 hours	29.03 / 20.64	22.03 / 15.44	19.41 / 13.99	Google-drive / 百度盘

Citation

@inproceedings{xu2023mononerd,
  title={Mononerd: Nerf-like representations for monocular 3d object detection},
  author={Xu, Junkai and Peng, Liang and Cheng, Haoran and Li, Hao and Qian, Wei and Li, Ke and Wang, Wenxiao and Cai, Deng},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={6814--6824},
  year={2023}
}

Acknowledgements

This project benefits from the following codebases. Thanks for their great works!

cskkxjk / MonoNeRD

readme