youshaamurhij/dagm-mono

Pytorch implementation for the paper: "DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction"

DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction

Optical Memory and Neural Networks paper link on Springer!

Introduction

This repo is the Pytorch Code of DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction.

Our work introduces DAGM-Mono, a monocular 3D pose and shape reconstruction algorithm. We address the challenge of detailed shape reconstruction by leveraging deformable attention mechanisms. Specifically, given 2D primitives, we reconstruct 3D object shapes using attention-guided modeling, considering the relevance between detected objects and vehicle shape priors. Our method introduces two additional loss functions: Chamfer Distance (CD) and Hierarchical Chamfer Distance to enhance the process of shape reconstruction. Additionally, our bi-contextual deformable attention framework estimates 3D object poses, capturing both inter-object relations and scene context. Extensive experiments demonstrate that DAGM-Mono achieves state-of-the-art performance on the ApolloCar3D dataset and significantly enhances the performance of mature monocular 3D object detectors.

DAGM-Mono Overview:

Qualitative results on ApolloCar3D - Projection on original images:

Qualitative results on ApolloCar3D - 3D mesh:

Main Results on ApolloCar3D - Car Instance Benchmark:

Main A3DP-Abs metrics on ApolloCar3D - Car Instance Benchmark (Validation Set)

Experiment	Mean	c0	c1	c2	c3	c4	c5	c6	c7	c8
Baseline	0.2363	0.4565	0.4195	0.3686	0.3197	0.2785	0.2204	0.155	0.099	0.0453
Dual Cross-Att.	0.2331	0.4561	0.4169	0.3682	0.3285	0.2642	0.2076	0.1485	0.0931	0.0376
Deformable Att.	0.2437	0.4621	0.4296	0.3873	0.3345	0.2801	0.2237	0.1646	0.106	0.0474
Chamfer loss	0.2349	0.4545	0.4122	0.3702	0.3277	0.2703	0.2122	0.1582	0.0986	0.0394
H. Cham. loss L2	0.2454	0.4659	0.4236	0.3905	0.3353	0.2805	0.2273	0.1713	0.1083	0.0474
H. Cham. loss L3	0.2476	0.4693	0.4318	0.3918	0.342	0.2859	0.2256	0.1661	0.1121	0.0501
H. Cham. + D-Att	0.2548	0.4734	0.438	0.3949	0.3494	0.2995	0.2402	0.1758	0.1142	0.0527

Main A3DP-Rel metrics on ApolloCar3D - Car Instance Benchmark (Validation Set)

Experiment	Mean	c0	c1	c2	c3	c4	c5	c6	c7	c8
Baseline	0.2189	0.4566	0.408	0.3627	0.2982	0.2479	0.1913	0.1246	0.0731	0.0237
Dual Cross-Att.	0.2134	0.4487	0.4033	0.349	0.2957	0.2378	0.1741	0.1179	0.0678	0.0291
Deformable Att.	0.2249	0.4593	0.4085	0.3683	0.3157	0.2515	0.1927	0.1376	0.0804	0.0336
Chamfer loss	0.2172	0.4443	0.4027	0.3554	0.3062	0.2467	0.1893	0.1254	0.0685	0.0293
H. Cham. loss L2	0.226	0.4652	0.4158	0.3671	0.314	0.2499	0.1955	0.1389	0.0796	0.0288
H. Cham. loss L3	0.2279	0.4609	0.4202	0.3657	0.3213	0.2515	0.2006	0.1401	0.0856	0.0304
H. Cham. + D-Att	0.2301	0.4646	0.4152	0.3657	0.3181	0.2631	0.2081	0.1384	0.0809	0.0355

Data setting

You need to set directory path as below

${CODE Root}
|-- data
    |-- apollo
        |-- train
            |-- apollo_annot
            |-- images
        |-- val
            |-- apollo_annot
            |-- images

• Download annotation files to fill in apollo_annot [here].

• Download image files to fill in images [here].

Usage:

For Docker container usage, run:

git clone DAGM-Mono
cd DAGM-Mono

bash docker/build.sh

bash docker/start.sh
bash docker/into.sh

From Conda environment usage, run:

conda create -n dagm_mono python=3.9
conda activate dagm_mono

#pytorch
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge

# detectron2
python -m pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html


conda install -c fvcore -c iopath -c conda-forge fvcore iopath

conda install -c bottler nvidiacub

conda install pytorch3d -c pytorch3d

pip install -r requirements.txt
pip install open3d==0.14.1

Train

1. To stable model convergence, we follow BAAM method to first train the 2D modules (Box, Keypoint). You can downlod pre-trained 2D module weights from here.
2. Replace it the third line of configs/custom.yaml.
3. Run the command below.

    python main.py -t

Inference

To run inference, update the weights path in the yaml config file and run:

    python main.py

Evaluation

1. Finish either inference process or train process.
2. Move to evaluation folder.
3. Run the command below.

    python eval.py \ 
        --light \
        --test_dir ../outputs/res \
        --gt_dir ../data/apollo/val/apollo_annot \
        --res_file test_results.txt

4. You can show A3DP results in test_results.txt.

Visualization

1. Install open3D python library
2. Move to 'vis' folder.
3. Run the command below.

    python vis_apollo.py \
        --output [path where the results are saved] \
        --file [file name to vis] \
        --save [path to save vis results]

License

DAGM-Mono is released under MIT license (see LICENSE). It is developed based on a forked version of BAAM. We also used code from OFMPNet, Swin-Transformer and FMFNet. Note that the used dataset (ApolloCar3D) is subject to their respective licenses and may not grant commercial use.

Citation:

If you find this work helpful, please consider citing:

@article{murhij2024dagm,
    title     = {DAGM-Mono: Deformable Attention-Guided Modeling for Monocular 3D Reconstruction},
    author    = {Murhij, Youshaa and Yudin, Dmitry},
    journal   = {Optical Memory and Neural Networks},
    volume    = {33},
    number    = {2},
    pages     = {144--156},
    year      = {2024},
    publisher = {Springer}
}

Contact:

Questions and suggestions are welcome!
Youshaa Murhij: yosha.morheg at phystech.edu