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MonoDetector

This repository contains unofficial pytorch implementations for MonoCon: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection (AAAI, 2022), IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection (JMLR, 2022), SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation (CVPRW, 2020), (MonoFlex) Objects are Different: Flexible Monocular 3D Object Detection (CVPR, 2021) and (GUPNet) Geometry Uncertainty Projection Network for Monocular 3D Object Detection (ICCV, 2021)

Features

Unchecked items are currently being prepared.

  • Support various mono3D detectors
  • KITTI Evaluation
  • Visualization (2D Bounding Box + Projected 3D Bounding Box)
  • Visualization (Bird Eye's View)
  • Video Inference using KITTI Raw Data Sequences
  • Multi-GPU Training
  • FPN

Quantitative Results

This repov3 includes instance depth head and ddp mode

3D Metric on Car Class

AP40@Easy AP40@Mod. AP40@Hard
Smoke(paper) 14.03 9.76 7.84
This Repo 18.62 (+4.59) 14.24 (+4.48) 12.72 (+4.88)
This Repo v2 20.45 (+6.42) 15.58 (+5.82) 13.20 (+5.36)
This Repo v3 21.24 (+7.21) 16.00 (+6.24) 13.47 (+5.63)
MonoFlex(paper) 23.64 17.51 14.83
This Repo 21.68 (-1.96) 15.64 (-1.87) 13.19 (-1.64)
GUPNet(paper) 22.76 16.46 13.72
This Repo 20.28 (-2.48) 14.39 (-2.07) 12.78 (-0.94)
MonoCon(paper) 26.33 19.03 16.00
This Repo 26.03 (-0.30) 19.02 (-0.01) 15.92 (-0.08)
IDMS(paper) 22.59 17.79 14.99
This Repo 20.40 (-2.19) 15.33 (-2.45) 12.94 (-2.05)

Preparations

Setup

# [Step 1]: Create new conda environment and activate.
#           Set [ENV_NAME] freely to any name you want. (Please exclude the brackets.)
conda create --name [ENV_NAME] python=3.9
conda activate [ENV_NAME]

# [Step 2]: Clone this repository and change directory.
git clone https://github.com/zhangcheng828/MonoDetector.git
cd monodetector

# [Step 3]: See https://pytorch.org/get-started/locally/ and install pytorch for your environment.
#           We have tested on version 1.11.0.
#           It is recommended to install version 1.7.0 or higher.

# [Step 4]: Install some packages using 'requirements.txt' in the repository.
pip install -r requirements.txt

Environments

We have tested our code in the following environment.

OS Python Pytorch CUDA GPU
Ubuntu 18.04.5 LTS 3.9.15 1.11.0 11.1 NVIDIA RTX 3090

Dataset

KITTI 3D Object Detection Dataset

Please download KITTI dataset from here.
Click the three items below from the link to download it and unzip it into the same folder.

  • Download left color images of object data set (12 GB)
  • Download camera calibration matrices of object data set (16 MB)
  • Download training labels of object data set (5 MB)

The structure of the data files should be as below.

[ROOT]
│
├── training
│   ├── calib
│   │   ├── 000000.txt
│   │   ├── 000001.txt
│   │   └── ...
│   ├── image_2
│   │   ├── 000000.png
│   │   ├── 000001.png
│   │   └── ...
│   └── label_2
│       ├── 000000.txt
│       ├── 000001.txt
│       └── ...
│
└── testing
    ├── calib
    ├── image_2
    └── label_2

Usage

Training

Just edit the items in config/default.py before execution.
If your GPU memory is less than 16GB, please set _C.USE_BENCHMARK in config/default.py to False.

python tools/train.py config/smoke.yaml

There is currently a bug, using local_rank 0 for model evaluation will cause other process to wait too long and report errors, so the default EVAL_PERIOD is set to -1.

torchrun --nproc_per_node=4 tools/train_ddp.py config/smoke.yaml # ddp mode

Evaluation

python tools/test.py  --config_file       [FILL]      # Config file (.yaml file)
                --checkpoint_file   [FILL]      # Checkpoint file (.pth file)
python tools/test.py --config_file=config/monocon.yaml --checkpoint_file=logs/monocon_12_08/checkpoints/epoch_198.pth

torchrun --nproc_per_node=1 tools/train_ddp.py config/smoke.yaml --eval=True # ddp mode

Inference

python tools/test.py  --config_file       [FILL]      # Config file (.yaml file)
                --checkpoint_file   [FILL]      # Checkpoint file (.pth file)
                --visualize                     # Perform visualization (Qualitative Results)
                --gpu_id            [Optional]  # Index of GPU to use for testing (Default: 0)
                --save_dir          [FILL]      # Path where visualization results will be saved to

Video Inference on KITTI Raw Dataset

The KITTI Raw Dataset can be downloaded by scene from here. (You will probably need to log in.)
There are 4 download links for each scene, of which, please download only "synced+rectified data" and "calibration".

python tools/test_raw.py  --data_dir          [FILL]      # Path where sequence images are saved
                    --calib_file        [FILL]      # Calibration file ("calib_cam_to_cam.txt")
                    --checkpoint_file   [FILL]      # Checkpoint file (.pth file)
                    --gpu_id            [Optional]  # Index of GPU to use for testing (Default: 0)
                    --fps               [Optional]  # FPS of the result video (Default: 25)
                    --save_dir          [FILL]      # Path of the directory to save the result video

Qualitative Results

Visualizations for 2D Boxes, 3D Boxes, and BEV, respectively, from top to bottom for each sample.

  • 000008.png (Validation Set)

  • 000134.png (Validation Set)

Change Log

This repository was last updated to v0.03 on 2022.12.14.

References

The following repositories were referred.