/Metric3D

The repo for "Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image" and "Metric3Dv2: A Versatile Monocular Geometric Foundation Model..."

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πŸš€ Metric3D Project πŸš€

Official PyTorch implementation 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

PWC

PWC

PWC

PWC

PWC

πŸ† Champion in CVPR2023 Monocular Depth Estimation Challenge

News

  • [2024/4/25] Weights for ViT-giant2 model released!
  • [2024/4/11] Training codes are released!
  • [2024/3/18] HuggingFace πŸ€— 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 strong and robust geometry 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. It can directly help you measure the size of structures from a single image. Now it achieves SOTA performance on over 10 depth and normal benchmarks.

depth_normal

metrology

πŸ“ Benchmarks

Metric Depth

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

Backbone KITTI Ξ΄1 ↑ KITTI Ξ΄2 ↑ KITTI AbsRel ↓ KITTI RMSE ↓ KITTI RMS_log ↓ NYU Ξ΄1 ↑ NYU Ξ΄2 ↑ NYU AbsRel ↓ NYU RMSE ↓ NYU log10 ↓
ZoeDepth ViT-Large 0.971 0.995 0.053 2.281 0.082 0.953 0.995 0.077 0.277 0.033
ZeroDepth ResNet-18 0.968 0.996 0.057 2.087 0.083 0.954 0.995 0.074 0.269 0.103
IEBins SwinT-Large 0.978 0.998 0.050 2.011 0.075 0.936 0.992 0.087 0.314 0.031
DepthAnything ViT-Large 0.982 0.998 0.046 1.985 0.069 0.984 0.998 0.056 0.206 0.024
Ours ViT-Large 0.985 0.998 0.999 1.985 0.064 0.989 0.998 0.047 0.183 0.020
Ours ViT-giant2 0.989 0.998 1.000 1.766 0.060 0.987 0.997 0.045 0.187 0.015

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.

#Data for Pretrain and Train KITTI Absrel ↓ KITTI Ξ΄1 ↑ NYUv2 AbsRel ↓ NYUv2 Ξ΄1 ↑ DIODE-Full AbsRel ↓ DIODE-Full Ξ΄1 ↑ Eth3d AbsRel ↓ Eth3d Ξ΄1 ↑
OmniData (v2, ViT-L) 1.3M + 12.2M 0.069 0.948 0.074 0.945 0.149 0.835 0.166 0.778
MariGold (LDMv2) 5B + 74K 0.099 0.916 0.055 0.961 0.308 0.773 0.127 0.960
DepthAnything (ViT-L) 142M + 63M 0.076 0.947 0.043 0.981 0.277 0.759 0.065 0.882
Ours (ViT-L) 142M + 16M 0.042 0.979 0.042 0.980 0.141 0.882 0.042 0.987
Ours (ViT-g) 142M + 16M 0.043 0.982 0.043 0.981 0.136 0.895 0.042 0.983

Surface Normal

Our models also show powerful performance on normal benchmarks.

NYU 11.25Β° ↑ NYU Mean ↓ NYU RMS ↓ ScanNet 11.25Β° ↑ ScanNet Mean ↓ ScanNet RMS ↓ iBims 11.25Β° ↑ iBims Mean ↓ iBims RMS ↓
EESNU 0.597 16.0 24.7 0.711 11.8 20.3 0.585 20.0 -
IronDepth - - - - - - 0.431 25.3 37.4
PolyMax 0.656 13.1 20.4 - - - - - -
Ours (ViT-L) 0.688 12.0 19.2 0.760 9.9 16.4 0.694 19.4 34.9
Ours (ViT-g) 0.662 13.2 20.2 0.778 9.2 15.3 0.697 19.6 35.2

🌈 DEMOs

Zero-shot monocular metric depth & surface normal

Zero-shot metric 3D recovery

Improving monocular SLAM

πŸ”¨ 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

Encoder Decoder Link
v1-T ConvNeXt-Tiny Hourglass-Decoder Coming soon
v1-L ConvNeXt-Large Hourglass-Decoder Download
v2-S DINO2reg-ViT-Small RAFT-4iter Download
v2-L DINO2reg-ViT-Large RAFT-8iter Download
v2-g DINO2reg-ViT-giant2 RAFT-8iter Download πŸ€—

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.

Metric3D and Droid-Slam

If you are interested in combining metric3D and monocular visual slam system to achieve the metric slam, you can refer to this repo.

❓ 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.

πŸ“§ Citation

@article{hu2024metric3dv2,
  title={Metric3D v2: 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},
  journal={arXiv preprint arXiv:2404.15506},
  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. Wei Yin [yvanwy@outlook.com] and Mr. Mu Hu [mhuam@connect.ust.hk].