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https://github.com/tai-wang/depth-from-motion
[ECCV 2022 oral] Monocular 3D Object Detection with Depth from Motion
https://github.com/tai-wang/depth-from-motion
3d-detection autonomous-driving monocular pytorch robotics structure-from-motion
Last synced: 4 days ago
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[ECCV 2022 oral] Monocular 3D Object Detection with Depth from Motion
- Host: GitHub
- URL: https://github.com/tai-wang/depth-from-motion
- Owner: Tai-Wang
- License: apache-2.0
- Created: 2022-07-20T14:46:38.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-11T11:52:03.000Z (about 2 years ago)
- Last Synced: 2024-10-26T21:35:16.917Z (18 days ago)
- Topics: 3d-detection, autonomous-driving, monocular, pytorch, robotics, structure-from-motion
- Language: Python
- Homepage:
- Size: 17.1 MB
- Stars: 304
- Watchers: 8
- Forks: 29
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Depth from Motion (DfM)
This repository is the official implementation for DfM and MV-FCOS3D++.
 
## Introduction
This is an official release of the paper: `Monocular 3D Object Detection with Depth from Motion` and `MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones`.
The code is still going through large refactoring. We plan to re-organize this repo as a combination of core codes for this project and mmdet3d requirement finally.
Please stay tuned for the clean release of all the configs and models.
Note: We will also release the refactored code in the official [mmdet3d](https://github.com/open-mmlab/mmdetection3d) soon.
> **Monocular 3D Object Detection with Depth from Motion**,
> Tai Wang, Jiangmiao Pang, Dahua Lin
> In: Proc. European Conference on Computer Vision (ECCV), 2022
> [[arXiv](https://arxiv.org/abs/2207.12988)][[Bibtex](https://github.com/Tai-Wang/Depth-from-Motion#citation)]
>
> **MV-FCOS3D++: Multi-View Camera-Only 4D Object Detection with Pretrained Monocular Backbones**,
> Tai Wang, Qing Lian, Chenming Zhu, Xinge Zhu, Wenwei Zhang
> In: arxiv, 2022
> [[arXiv](https://arxiv.org/abs/2207.12716)][[Bibtex](https://github.com/Tai-Wang/Depth-from-Motion#citation)]
## Results
### DfM
The results of DfM and its corresponding config are shown as below.
We have released the preliminary model for reproducing the results on the KITTI validation set.
The complete model checkpoints and logs will be released soon.
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | Easy | Moderate | Hard | Download|
| :-------: | :-----: | :------: | :------------: | :----: | :------: | :--: | :-----: |
| [ResNet34](./configs/dfm/dfm_r34_1x8_kitti-3d-3class.py) | - | - | - | 29.1232 | 19.8970 | 17.39101 | [model](https://download.openmmlab.com/mim-example/dfm/dfm_r34_1x8_kitti-3d-3class/epoch_53.pth) \| [log](https://download.openmmlab.com/mim-example/dfm/dfm_r34_1x8_kitti-3d-3class/20220909_092821.log.json) |
| above @ BEV AP
(IoU 0.7) | - | - | - | 38.9137 | 27.2843 | 24.8381 | |
| above @ 3D AP
(IoU 0.5) | - | - | - | 67.4935 | 51.2602 | 47.4430 | |
| above @ BEV AP
(IoU 0.5) | - | - | - | 72.5696 | 55.4583 | 52.4735 | |
[1] This reproduced performance may have some degree of fluctuation due to the limited training samples and sensitive metrics. From my experience of multiple runs, the average performance may vary from 26/18/16 to 29/20/17, depending on the effect of corner cases (caused by matrix inverse computation or other reasons). Please stay tuned for a more stable version. (Models and logs will be updated soon.)
### MV-FCOS3D++
The results of MV-FCOS3D++ (baseline version) and its corresponding config are shown as below.
We have released the preliminary config for reproducing the results on the Waymo validation set.
(To comply the license agreement of Waymo dataset, the pre-trained models on Waymo dataset are not released.)
The complete model configs and logs will be released soon.
#### Pretrained FCOS3D++ (without customized finetuning)
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAPL | mAP | mAPH | Download |
| :-------: | :-----: | :------: | :------------: | :----: | :------: | :--: | :-----: |
| [ResNet101+DCN](./configs/pgd/pgd_r101_fpn_gn-head_dcn_3x16_2x_waymoD3-mv3d.py) | - | - | - | 20.41 | 28.6 | 27.01 | [log](https://download.openmmlab.com/mim-example/dfm/pgd_r101_fpn_gn-head_dcn_3x16_2x_waymoD3-mv3d/20220808_221519.log.json) |
| above @ Car | - | - | - | 41.05 | 55.74 | 54.83 | |
| above @ Pedestrian | - | - | - | 18.77 | 27.85 | 24.21 | |
| above @ Cyclist | - | - | - | 1.43 | 2.21 | 2.0 | |
#### MV-FCOS3D++ with Pretrained FCOS3D++
| Backbone | Lr schd | Mem (GB) | Inf time (fps) | mAPL | mAP | mAPH | Download |
| :-------: | :-----: | :------: | :------------: | :----: | :------: | :--: | :-----: |
| [ResNet101+DCN](./configs/dfm/multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class_camsync.py) | - | - | - | 33.8 | 46.65 | 44.25| [log](https://download.openmmlab.com/mim-example/dfm/multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class_camsync/20220807_153735.log.json) |
| above @ Car | - | - | - | 52.69 | 68.36 | 67.47 | |
| above @ Pedestrian | - | - | - | 26.82 | 38.47 | 34.1 | |
| above @ Cyclist | - | - | - | 21.9 | 33.11 | 31.16 | |
| [ResNet101+DCN
+10 sweeps](./configs/dfm/multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class_camsync_10sweeps.py) | - | - | - | 35.14| 47.98 | 45.49 | [log1](https://download.openmmlab.com/mim-example/dfm/multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class_camsync_10sweeps/20220808_170010.log.json) \| [log2](https://download.openmmlab.com/mim-example/dfm/multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class_camsync_10sweeps/20220809_093358.log.json) |
| above @ Car | - | - | - | 55.44 | 70.72 | 69.79 | |
| above @ Pedestrian | - | - | - | 27.6 | 39.5 | 35.1 | |
| above @ Cyclist | - | - | - | 22.39 | 33.72 | 31.59 | |
| [ResNet101+DCN
(slow infer)2](./configs/dfm/multiview-dfm_r101_dcn_2x16_waymoD5-3d-3class_camsync.py) | - | - | - | 37.9 | 52.15 | 48.84| |
| above @ Car | - | - | - | 56.24 | 73.15 | 72.07 | |
| above @ Pedestrian | - | - | - | 34.6 | 49.01 | 42.25 | |
| above @ Cyclist | - | - | - | 22.84 | 34.29 | 32.18 | |
[2] "slow infer" refers to changing the nms setting to `nms_pre=4096` and `max_num=500` to increase the number of predictions such that the inference can get a better recall performance. It will slow down the inference procedure but significantly improves the final performance under the Waymo metric. **The same trick can also be applied to the 10-sweep config and other models.**
## Installation
It requires the following OpenMMLab packages:
- MMCV-full >= v1.6.0 (recommended for the latest iou3d computation)
- MMDetection >= v2.24.0
- MMSegmentation >= v0.20.0
All the above versions are recommended except mmcv. Lower version of mmdet and mmseg may also work but are not tested temporarily.
Example commands are shown as follows.
```bash
conda create --name dfm python=3.7 -y
conda activate dfm
conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install mmcv-full==1.6.0
pip install mmdet==2.24.0
pip install mmsegmentation==0.20.0
git clone https://github.com/Tai-Wang/Depth-from-Motion.git
cd Depth-from-Motion
pip install -v -e .
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Usage
### Data preparation
First prepare the raw data of KITTI and Waymo data following [MMDetection3D](https://github.com/open-mmlab/mmdetection3d).
Then we prepare the data related to temporally consecutive frames.
For KITTI, we need to additionally download the pose and label files of the raw data [here](https://www.cse.msu.edu/computervision/Kinematic3D-raw_extra.zip) and the official mapping (between the raw data and the 3D detection benchmark split) [here](https://github.com/garrickbrazil/kinematic3d/tree/master/data/kitti_split1/devkit/mapping). Then we can run the data converter script:
```
python tools/create_data.py kitti --root-path ./data/kitti --out-dir ./data/kitti --extra-tag kitti
```
For Waymo, we need to additionally generate the ground truth bin file for camera-only setting (only boxes covered by the perception range of cameras are considered). Besides, we recommend use the latest waymo dataset, which includes the camera synced annotations tailored to this setting.
```
python tools/create_waymo_gt_bin.py
```
Then please follow the mmdet3d [tutorial for Waymo dataset](https://mmdetection3d.readthedocs.io/en/latest/datasets/waymo_det.html) for the pre-processing steps.
The final data structure looks like below:
```text
mmdetection3d
├── mmdet3d
├── tools
├── configs
├── data
│ ├── kitti
│ │ ├── ImageSets
│ │ ├── testing
│ │ │ ├── calib
│ │ │ ├── image_2
│ │ │ ├── prev_2
│ │ │ ├── velodyne
│ │ ├── training
│ │ │ ├── calib
│ │ │ ├── image_2
│ │ │ ├── prev_2
│ │ │ ├── label_2
│ │ │ ├── velodyne
│ │ ├── raw
│ │ │ ├── 2011_09_26_drive_0001_sync
│ │ │ ├── xxxx (other raw data files)
│ │ ├── devkit
│ │ │ ├── mapping
│ │ │ │ ├── train_mapping.txt
│ │ │ │ ├── train_rand.txt
│ ├── waymo
│ │ ├── waymo_format
│ │ │ ├── training
│ │ │ ├── validation
│ │ │ ├── testing
│ │ │ ├── gt.bin
│ │ │ ├── cam_gt.bin
│ │ ├── kitti_format
│ │ │ ├── ImageSets
│ │ │ ├── training
│ │ │ │ ├── calib
│ │ │ │ ├── image_0
│ │ │ │ ├── image_1
│ │ │ │ ├── image_2
│ │ │ │ ├── image_3
│ │ │ │ ├── image_4
│ │ │ │ ├── label_0
│ │ │ │ ├── label_1
│ │ │ │ ├── label_2
│ │ │ │ ├── label_3
│ │ │ │ ├── label_4
│ │ │ │ ├── label_all
│ │ │ │ ├── pose
│ │ │ │ ├── velodyne
│ │ │ ├── testing
│ │ │ │ ├── (the same as training)
│ │ │ ├── waymo_gt_database
│ │ │ ├── waymo_infos_trainval.pkl
│ │ │ ├── waymo_infos_train.pkl
│ │ │ ├── waymo_infos_val.pkl
│ │ │ ├── waymo_infos_test.pkl
│ │ │ ├── waymo_dbinfos_train.pkl
```
### Pretrained models
For the KITTI implementation of DfM, we keep the LIGA-Stereo setting that has a LiDAR-based teacher for better supervision during training. Please download the teacher checkpoint (has been converted to mmdet3d-style) [here](https://download.openmmlab.com/mim-example/dfm/pretrained_models/mmdet3d-second-teacher.pth). It can make this network converge faster and bring ~1 AP performance gain. We will consider to replace it with other more direct supervision for simpler usage in the near future.
### Demo
To test DfM on image data, simply run:
```shell
python demo/mono_det_demo.py ${IMAGE_FILE} ${ANNOTATION_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${GPU_ID}] [--out-dir ${OUT_DIR}] [--show]
```
where the `ANNOTATION_FILE` should provide the 3D to 2D projection matrix (camera intrinsic matrix). The visualization results including an image and its predicted 3D bounding boxes projected on the image will be saved in `${OUT_DIR}/IMAGE_NAME`.
Example on KITTI data using [DfM](https://github.com/Tai-Wang/Depth-from-Motion/blob/main/configs/dfm) model:
```shell
python demo/mono_det_demo.py demo/data/kitti/000008.png demo/data/kitti/kitti_000008_infos.pkl configs/dfm/dfm_r34_1x8_kitti-3d-3class.py checkpoints/dfm.pth
```
### Training and testing
For training and testing, you can follow the standard command in mmdet to train and test the model
```bash
# train DfM on KITTI
./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
```
For simple inference and evaluation, you can use the command below:
```bash
# evaluate DfM on KITTI and MV-FCOS3D++ on Waymo
./tools/slurm_test.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${CKPT_PATH} --eval mAP
```
### FAQ
- How to use the Waymo LET-AP metric to evaluate the performance of MV-FCOS3D++?
You can follow the [instruction](https://github.com/waymo-research/waymo-open-dataset/blob/master/docs/quick_start.md#metrics-computation) of compiling the original Waymo detection metrics to compile this [file](https://github.com/waymo-research/waymo-open-dataset/blob/master/waymo_open_dataset/metrics/tools/compute_detection_let_metrics_main.cc) and get the `compute_detection_let_metrics_main` file for LET-AP metric evaluation. Besides, you can refer to the [official tutorial](https://github.com/waymo-research/waymo-open-dataset/blob/master/tutorial/tutorial_camera_only.ipynb) of camera-only 3D detection for more details about its python example code.
## Acknowledgement
This codebase is based on [MMDet3D](https://github.com/open-mmlab/mmdetection3d) and it benefits a lot from [LIGA-Stereo](https://github.com/xy-guo/LIGA-Stereo).
## Citation
```bibtex
@inproceedings{wang2022dfm,
title={Monocular 3D Object Detection with Depth from Motion},
author={Wang, Tai and Pang, Jiangmiao and Lin, Dahua},
year={2022},
booktitle={European Conference on Computer Vision (ECCV)},
}
@article{wang2022mvfcos3d++,
title={{MV-FCOS3D++: Multi-View} Camera-Only 4D Object Detection with Pretrained Monocular Backbones},
author={Wang, Tai and Lian, Qing and Zhu, Chenming and Zhu, Xinge and Zhang, Wenwei},
journal={arXiv preprint},
year={2022}
}
```