metric_depth

Depth Anything for Metric Depth Estimation

Our Depth Anything models primarily focus on robust relative depth estimation. To achieve metric depth estimation, we follow ZoeDepth to fine-tune from our Depth Anything pre-trained encoder with metric depth information from NYUv2 or KITTI.

Performance

In-domain metric depth estimation

NYUv2

Method	$\delta_1 \uparrow$	$\delta_2 \uparrow$	$\delta_3 \uparrow$	AbsRel $\downarrow$	RMSE $\downarrow$	log10 $\downarrow$
ZoeDepth	0.951	0.994	0.999	0.077	0.282	0.033
Depth Anything	0.984	0.998	1.000	0.056	0.206	0.024

KITTI

Method	$\delta_1 \uparrow$	$\delta_2 \uparrow$	$\delta_3 \uparrow$	AbsRel $\downarrow$	RMSE $\downarrow$	log10 $\downarrow$
ZoeDepth	0.971	0.996	0.999	0.054	2.281	0.082
Depth Anything	0.982	0.998	1.000	0.046	1.896	0.069

Zero-shot metric depth estimation

Indoor: NYUv2 $\rightarrow$ SUN RGB-D, iBims-1, and HyperSim
Outdoor: KITTI $\rightarrow$ Virtual KITTI 2 and DIODE Outdoor

Method	SUN		iBims		HyperSim		vKITTI		DIODE Outdoor
	AbsRel	$\delta_1$	AbsRel	$\delta_1$	AbsRel	$\delta_1$	AbsRel	$\delta_1$	AbsRel	$\delta_1$
ZoeDepth	0.520	0.545	0.169	0.656	0.407	0.302	0.106	0.844	0.814	0.237
Depth Anything	0.500	0.660	0.150	0.714	0.363	0.361	0.085	0.913	0.794	0.288

Pre-trained models

We provide two pre-trained models (download), one for indoor metric depth estimation trained on NYUv2, and the other for outdoor metric depth estimation trained on KITTI.

Installation

conda env create -n depth_anything_metric --file environment.yml
conda activate depth_anything_metric

Please follow ZoeDepth to prepare the training and test datasets.

Evaluation

Make sure you have downloaded our pre-trained models here and put them under the checkpoints directory.

Indoor:

python evaluate.py -m zoedepth --pretrained_resource="local::./checkpoints/depth_anything_metric_depth_indoor.pt" -d <nyu | sunrgbd | ibims | hypersim_test>

Outdoor:

python evaluate.py -m zoedepth --pretrained_resource="local::./checkpoints/depth_anything_metric_depth_outdoor.pt" -d <kitti | vkitti2 | diode_outdoor>

Training

python train_mono.py -m zoedepth -d <nyu | kitti> --pretrained_resource=""

This will automatically use our Depth Anything pre-trained ViT-L encoder.

Citation

If you find this project useful, please consider citing:

@article{depthanything,
      title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
      author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
      journal={arXiv:2401.10891},
      year={2024},
}