Depth3D: A Model Zoo for Robust Monocular Metric Depth Estimation

Depth3D aims at robust monocular metric depth estimation on zero-shot testing images, while ensuring the geometric accuracy of unprojected 3D point cloud. We release models of BEiT-L, ConvNext-L, and Swin2-L trained on 11.8 million RGB-D data. The technical report is avaliable in Depth3D/technical_report_v1.1.pdf.

Dataset and Weight Download

Download all the folders including datasets, weights, pretrained_weights, weights_ablation, and place them under Depth3D/. The download link is as follows:

Baidu Netdisk: 链接(Download link): https://pan.baidu.com/s/1ISB0kOooYz5QMttmHAd5cA?pwd=qr8q 提取码(Passcode): qr8q

The Components of Each Folder

- datasets # The test datasets, we provide kitti, nyu, scannet, 7scenes, diode, eth3d, ibims, and nuscenes.
- pretrained weights # pre-trained weights, which are adopted for training depth models.
	- convnext_large_22k_1k_384.pth # Weight of ConvNext-L pre-trained on ImageNet-22k and fine-tuned on ImageNet-1k at resolution 384x384.
	- dpt_beit_large_512.pt # The highest quality affine-invariant depth model of BEiT-L trained by MiDaS 3.1 (https://github.com/isl-org/MiDaS/tree/master).
	- dpt_swin2_large_384.pt # The speed-performance trade-off affine-invariant depth model of Swin2-L trained by MiDaS 3.1.
- weights # Our released models, we leverage around 12 million RGB-D data for training.
	- metricdepth_beit_large_512x512.pth # BEiT-L depth model trained on the resolution of 512x512. Best Performance.
	- metricdepth_convnext_large_544x1216.pth # ConvNexrt-L depth model trained on the resolution of 544x1216.
	- metricdepth_swin2_large_384x384.pth # Swin2-L depth model trained on the resolution of 384x384. Balance between speed and quality.
- weights_ablation # Checkpoints of ablation study
	- ablation_full.pth # Full baseline of ablation study. Trained with 21 datasets, 7 losses, and supervised in the camera canonical space.
	- ablation_data_6_datasets.pth # Compared to ablation_full.pth, only trained on 6 datasets.
	- ablation_data_13_datasets.pth # Compared to ablation_full.pth, only trained on 13 datasets.
	- ablation_loss_l1_sky.pth # Compared to ablation_full.pth, only supervised with "L1Loss" and "SkyRegularizationLoss".
	- ablation_loss_l1_sky_normal.pth # Compared to ablation_full.pth, only supervised with "L1Loss" and "SkyRegularizationLoss", "VNLoss", "EdgeguidedNormalLoss", "PWNPlanesLoss".
	- ablation_wo_lablescalecanonical.pth # Compared to ablation_full.pth, it does not transform the camera to the canonical space, which results in unsatisfactory metric depth performance.

Reproducing the Results of the Technical Report

To reproduce the results, please run the scripts of Depth3D/scripts/technical_report. For example. if you would like to reproduce the Table 3, run this commmand: python scripts/technical_report/run_table3.py. It will take hours of time to output the final results. See scripts/technical_report/README.md for details.

Structure of Code

- Depth3D
	- data_info
		- check_datasets.py
		- pretrained_weight.py # pretrained weight path of backbone.
		- public_datasets.py # path of annotations of diverse datasets.
	- datasets
		- ibims
			- ibims
			- test_annotation.json
		- diode
			- diode
			- test_annotation.json
			- test_annotation_indoor.json
			- test_annotation_outdoor.json
		- ETH3D
			- ETH3D
			- test_annotations.json
		...
	- demo_data # demo data of technical report.
	- mono
		- configs # configs of training and evaluation.
		- datasets # torch.utils.data.Dataset.
		- model # depth models.
		- tools
		- utils
	- other_tools
	- pretrained_weights # pretrained weights, used for training depth models.
	- scripts # scripts of training and testing
		- ablation 
			- test
			- train
		- technical_report # scripts to reproduce the results of technical report.
		- test
		- train
	- show_dirs # output folder of inference.
	- weights # place our trained depth models here.
	- weights_ablation # place our released depth models of ablation study here.
	- work_dirs # output folder of training.

Installation

conda create -n Depth3D python=3.7
conda activate Depth3D
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
pip install -U openmim
mim install mmengine
mim install "mmcv-full==1.3.17"
pip install yapf==0.40.1

For 40 Series GPUs

conda create -n Depth3D python=3.8
conda activate Depth3D
pip install torch==2.0.0 torchvision==0.15.1
pip install -r requirements.txt
pip install -U openmim
mim install mmengine
mim install "mmcv-full==1.7.1"
pip install yapf==0.40.1

Evaluation of a Specific Dataset

Case 1: Metric Depth Estimation of Our Provided Test Datasets

source scripts/test/beit/test_beit_nyu.sh # change the shell file path if necessary.

If you would like to evaluate on a customized dataset, let's take "demo_data" as an example:

Case 2: Metric Depth Estimation of Customized Data

1. Generate test_annotation.json of customized dataset:

dict(
	'files': [
		dict('cam_in': [fx, fy, cx, cy], 'rgb': 'demo_data/rgb/xxx.png', (optional) 'depth': 'demo_data/gt_depth/xxx.npy', (optional) 'depth_scale': 1.0, (optional) 'depth_mask': 'demo_data/gt_depth_mask/xxx.npy'), 
		dict('cam_in': [fx, fy, cx, cy], 'rgb': 'demo_data/rgb/xxx.png', (optional) 'depth': 'demo_data/gt_depth/xxx.png', (optional) 'depth_scale': 256.0, (optional) 'depth_mask': null), 
		dict('cam_in': [fx, fy, cx, cy], 'rgb': 'demo_data/rgb/xxx.png', (optional) 'depth': 'demo_data/gt_depth/xxx.png', (optional) 'depth_scale': 1000.0, (optional) 'depth_mask': null), 
		...
	]
)

See Depth3D/demo_data/test_annotations.json for details. The depth and depth_scale are necessary if evaluation is expected. The depth_scale stands for the depth scale ratio of depth image. The depth_mask is used to filter out invalid depth regions, with 0 for invalid regions and others for valid regions.

We store the relative file path in the annotation. For example, if the RGB file path is /mnt/nas/share/home/xugk/Depth3D/demo_data/rgb/0016028039294718_b.jpg, we save the relative path demo_data/rgb/0016028039294718_b.jpg, and set parameters "$DATA_ROOT" to "/mnt/nas/share/home/xugk/Depth3D/"(See step 2 below).

2. Inference with this script:

DEPTH3D_TEST_ANNO_PATH='demo_data/test_annotations.json'
DATA_ROOT='/mnt/nas/share/home/xugk/Depth3D/'
source scripts/inference/inference_metric_depth.sh $TEST_ANNO_PATH $DATA_ROOT

If the absolute file paths are saved in test_annotation.json, you can simply input the "$TEST_ANNO_PATH" only:

TEST_ANNO_PATH='demo_data/test_annotations_absolute_path.json'
source scripts/inference/inference_metric_depth.sh $TEST_ANNO_PATH

3. The output depth maps and point clouds are saved in Depth3D/outputs_beit_metric_depth.

Case 3: Scale-invariant Depth Estimation of Customized Data (In the Wild Images, Unknown Focal Length)

Assuming the path of RGB folder is "demo_data/rgb/":

RGB_FOLDER='demo_data/rgb/'
source scripts/inference/inference_in_the_wild.sh $RGB_FOLDER

The output depth maps and point clouds are saved in Depth3D/outputs_beit_in_the_wild.

Dataset Structure

We use the *_annotation.json files to store the camera intrinsic information and the paths of rgb, depth, etc. The data structure is as follows:

- Taskonomy
	- Taskonomy
		- (optional) meta # save the pickle files, see 'Format 1' for details
		- rgb
		- depth
		- (optional) sem
		- (optional) normal
	- test_annotation.json # test annotation file
	- train_annotation.json # train annotation file

Format 1

The format of *_annotation.json files：

dict(
	'files': [
		dict('cam_in': [fx, fy, cx, cy], 'rgb': 'Taskonomy/rgb/xxx.png', 'depth': 'Taskonomy/depth/xxx.png', (optional) 'sem': 'Taskonomy/sem/xxx.png', (optional) 'normal': 'Taskonomy/norm/xxx.png'),
		dict('cam_in': [fx, fy, cx, cy], 'rgb': 'Taskonomy/rgb/xxx.png', 'depth': 'Taskonomy/depth/xxx.png', (optional) 'sem': 'Taskonomy/sem/xxx.png', (optional) 'normal': 'Taskonomy/norm/xxx.png'),
		dict('cam_in': [fx, fy, cx, cy], 'rgb': 'Taskonomy/rgb/xxx.png', 'depth': 'Taskonomy/depth/xxx.png', (optional) 'sem': 'Taskonomy/sem/xxx.png', (optional) 'normal': 'Taskonomy/norm/xxx.png'),
		...
	]
)

Format 2

The format of *_annotation.json files：

dict(
	'files': [
		dict('meta_data': 'Taskonomy/xxx/xxx.pkl'),
		dict('meta_data': 'Taskonomy/xxx/xxx.pkl'),
		dict('meta_data': 'Taskonomy/xxx/xxx.pkl'),
		...
	]
)

The format of 'xxx.pkl'：

dict(
	'cam_in': [fx, fy, cx, cy],
	'rgb': 'Taskonomy/rgb/xxx.png',
	'depth': 'Taskonomy/depth/xxx.png',
	(optional) 'sem': 'Taskonomy/sem/xxx.png'
	(optional) 'normal': 'Taskonomy/norm/xxx.png',
)

Demos

Monocular Depth Estimation

Unprojected 3D Point Cloud

🎫 License

For non-commercial academic use, this project is licensed under the 2-clause BSD License. For commercial use, please contact Chunhua Shen.