🚀 Metric3D Project 🚀

Official Model card of Metric3Dv1 and Metric3Dv2:

[1] Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image

[2] Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation

< Project page | Metric3D paper | Metric3Dv2 paper(on hold) | Demo | Model card >

News and TO DO LIST

• Droid slam codes

• Release the ViT-giant2 model

• Focal length free mode

• Floating noise removing mode

• Improving HuggingFace Demo and Visualization

• [2024/4/11] Training codes are released!

• [2024/3/18] HuggingFace GPU version updated!

• [2024/3/18] Project page released!

• [2024/3/18] Metric3D V2 models released, supporting metric depth and surface normal now!

• [2023/8/10] Inference codes, pre-trained weights, and demo released.

• [2023/7] Metric3D accepted by ICCV 2023!

• [2023/4] The Champion of 2nd Monocular Depth Estimation Challenge in CVPR 2023

🌼 Abstract

Metric3D is a versatile geometric foundation model for high-quality and zero-shot metric depth and surface normal estimation from a single image. It excels at solving in-the-wild scene reconstruction.

📝 Benchmarks

Metric Depth

Our models rank 1st on the routing KITTI and NYU benchmarks.

Affine-invariant Depth

Even compared to recent affine-invariant depth methods (Marigold and Depth Anything), our metric-depth (and normal) models still show superior performance.

Surface Normal

Our models also show powerful performance on normal benchmarks.

🌈 DEMOs

Zero-shot monocular metric depth & surface normal

Zero-shot metric 3D recovery

Improving monocular SLAM

🔨 Installation

One-line Installation

For the ViT models, use the following environment：

pip install -r requirements_v2.txt

For ConvNeXt-L, it is

pip install -r requirements_v1.txt

dataset annotation components

With off-the-shelf depth datasets, we need to generate json annotaions in compatible with this dataset, which is organized by:

dict(
    'files':list(
        dict(
            'rgb': 'data/kitti_demo/rgb/xxx.png',
            'depth': 'data/kitti_demo/depth/xxx.png',
            'depth_scale': 1000.0 # the depth scale of gt depth img.
            'cam_in': [fx, fy, cx, cy],
        ),

        dict(
            ...
        ),

        ...
    )
)

To generate such annotations, please refer to the "Inference" section.

configs

In mono/configs we provide different config setups.

Intrinsics of the canonical camera is set bellow:

    canonical_space = dict(
        img_size=(512, 960),
        focal_length=1000.0,
    ),

where cx and cy is set to be half of the image size.

Inference settings are defined as

    depth_range=(0, 1),
    depth_normalize=(0.3, 150),
    crop_size = (512, 1088),

where the images will be first resized as the crop_size and then fed into the model.

✈️ Training

Please refer to training/README.md

✈️ Inference

Download Checkpoint

Dataset Mode

1. put the trained ckpt file model.pth in weight/.
2. generate data annotation by following the code data/gene_annos_kitti_demo.py, which includes 'rgb', (optional) 'intrinsic', (optional) 'depth', (optional) 'depth_scale'.
3. change the 'test_data_path' in test_*.sh to the *.json path.
4. run source test_kitti.sh or source test_nyu.sh.

In-the-Wild Mode

1. put the trained ckpt file model.pth in weight/.
2. change the 'test_data_path' in test.sh to the image folder path.
3. run source test_vit.sh for transformers and source test.sh for convnets. As no intrinsics are provided, we provided by default 9 settings of focal length.

❓ Q & A

Q1: Why depth maps look good but pointclouds are distorted?

Because the focal length is not properly set! Please find a proper focal length by modifying codes here yourself.

Q2: Why the pointclouds are too slow to be generated?

Because the images are too large! Use smaller ones instead.

Q3: Why predicted depth maps are not satisfactory?

First be sure all black padding regions at image boundaries are cropped out. Then please try again. Besides, metric 3D is not almighty. Some objects (chandeliers, drones...) / camera views (aerial view, bev...) do not occur frequently in the training datasets. We will going deeper into this and release more powerful solutions.

📧 Citation

@article{hu2024metric3dv2,
  title={A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation},
  author={Hu, Mu and Yin, Wei, and Zhang, Chi and Cai, Zhipeng and Long, Xiaoxiao and Chen, Hao, and Wang, Kaixuan and Yu, Gang and Shen, Chunhua and Shen, Shaojie},
  booktitle={arXiv},
  year={2024}
}

@article{yin2023metric,
  title={Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image},
  author={Wei Yin, Chi Zhang, Hao Chen, Zhipeng Cai, Gang Yu, Kaixuan Wang, Xiaozhi Chen, Chunhua Shen},
  booktitle={ICCV},
  year={2023}
}

License and Contact

The Metric 3D code is under a 2-clause BSD License for non-commercial usage. For further questions, contact Dr. yvan.yin [yvanwy@outlook.com] and Mr. mu.hu [mhuam@connect.ust.hk].