MaskingDepth (IROS 2024)

[Project Page] [Paper]

This code is the implementation of the paper MaskingDepth: Masked Consistency Regularization for Semi-supervised Monocular Depth Estimation by Baek et al.

We propose MaskingDepth, a novel semi-supervised learning framework for monocular depth estimation to mitigate the reliance on large ground-truth depth quantities. MaskingDepth is designed to enforce consistency between the strongly-augmented unlabeled data and the pseudo-labels derived from weakly-augmented unlabeled data, which enables learning depth without supervision. In this framework, a novel data augmentation is proposed to take the advantage of a naive masking strategy as an augmentation, while avoiding its scale ambiguity problem between depths from weakly- and strongly-augmented branches and risk of missing small-scale instances. To only retain high-confident depth predictions from the weakly-augmented branch as pseudo-labels, we also present an uncertainty estimation technique, which is used to define robust consistency regularization. Experiments on KITTI and NYU-Depth-v2 datasets demonstrate the effectiveness of each component, its robustness to the use of fewer depth-annotated images, and superior performance compared to other state-of-the-art semi-supervised methods for monocular depth estimation.

Environment

• NGC pytorch 20.11-py3 (Docker container)
• additional require packages (dotmap, wandb, einops, timm)
• NVIDIA RGX 3090s

In docker container

git clone https://github.com/KU-CVLAB/MaskingDepth.git   # Download this project
cd MaskingDepth                                          # Change directory
sh package_install.sh                                    # Install additionally package 

we recommend that vit encoder initialize through this weight file. Download ViT ImageNet pretrained weight

Dataset

• KITTI
• NYU

Training

• Supervised learning:

Edit conf/base_train.yaml file. See the comments in the configuration file for detail options.

python train.py

• Semi-supervised learning

Edit conf/consistency_train.yaml file. See the comments in the configuration file for detail options.

python consistency_train.py

Evaluation

We evaluate through the eval_with_pngs.py created by BTS. For evaluation we divide test set according to Eigen split.

Results

Quantitative results on the KITTI dataset in a sparsely-supervised setting

full

Methods	AbsRel ↓	SqRel ↓	RMSE ↓	RMSElog ↓	δ↑
Baseline	0.076 ± 0.003	0.365 ± 0.004	3.290 ± 0.015	0.118 ± 0.001	0.934 ± 0.001
Baseline+Self	0.076 ± 0.002	0.367 ± 0.007	3.291 ± 0.020	0.117 ± 0.001	0.933 ± 0.002
Ours+Self	0.079 ± 0.001	0.379 ± 0.007	3.388 ± 0.019	0.121 ± 0.009	0.929 ± 0.001
Ours	0.074 ± 0.001	0.362 ± 0.001	3.253 ± 0.012	0.116 ± 0.001	0.935 ± 0.001

10,000

Methods	AbsRel ↓	SqRel ↓	RMSE ↓	RMSElog ↓	δ↑
Baseline	0.079 ± 0.001	0.379 ± 0.007	3.388 ± 0.019	0.121 ± 0.009	0.929 ± 0.001
Baseline+Self	0.078 ± 0.001	0.376 ± 0.006	3.347 ± 0.043	0.119 ± 0.002	0.931 ± 0.001
Ours+Self	0.076 ± 0.017	0.369 ± 0.004	3.311 ± 0.011	0.117 ± 0.001	0.935 ± 0.002
Ours	0.075 ± 0.002	0.362 ± 0.006	3.259 ± 0.020	0.116 ± 0.001	0.934 ± 0.003

1,000

Methods	AbsRel ↓	SqRel ↓	RMSE ↓	RMSElog ↓	δ↑
Baseline	0.098 ± 0.004	0.515 ± 0.030	3.785 ± 0.013	0.142 ± 0.005	0.899 ± 0.005
Baseline+Self	0.096 ± 0.002	0.523 ± 0.024	3.750 ± 0.033	0.140 ± 0.002	0.900 ± 0.004
Ours+Self	0.085 ± 0.017	0.430 ± 0.011	3.521 ± 0.012	0.129 ± 0.012	0.918 ± 0.010
Ours	0.088 ± 0.003	0.419 ± 0.007	3.490 ± 0.020	0.129 ± 0.003	0.917 ± 0.002

100

Methods	AbsRel ↓	SqRel ↓	RMSE ↓	RMSElog ↓	δ↑
Baseline	0.135 ± 0.005	0.728 ± 0.019	4.585 ± 0.048	0.186 ± 0.011	0.831 ± 0.005
Baseline+Self	0.132 ± 0.004	0.759 ± 0.014	4.559 ± 0.044	0.184 ± 0.003	0.834 ± 0.004
Ours+Self	0.123 ± 0.003	0.747 ± 0.018	4.497 ± 0.042	0.181 ± 0.005	0.839 ± 0.005
Ours	0.128 ± 0.004	0.707 ± 0.013	4.295 ± 0.037	0.173 ± 0.006	0.849 ± 0.006

10

Methods	AbsRel ↓	SqRel ↓	RMSE ↓	RMSElog ↓	δ↑
Baseline	0.201 ± 0.023	1.508 ± 0.045	6.163 ± 0.082	0.268 ± 0.029	0.701 ± 0.021
Baseline+Self	0.210 ± 0.020	1.322 ± 0.042	5.627 ± 0.080	0.265 ± 0.027	0.711 ± 0.016
Ours+Self	0.184 ± 0.011	1.265 ± 0.064	5.747 ± 0.080	0.243 ± 0.007	0.727 ± 0.018
Ours	0.197 ± 0.019	1.378 ± 0.032	5.650 ± 0.091	0.261 ± 0.030	0.723 ± 0.017

Quantitative results on the NYU-Depth-v2 dataset in a sparsely-supervised setting

full

Methods	AbsRel ↓	RMSE ↓	log10 ↓	δ↑
Baseline	0.106 ± 0.002	0.380 ± 0.004	0.053 ± 0.001	0.897 ± 0.001
Ours	0.105 ± 0.002	0.379 ± 0.003	0.053 ± 0.001	0.899 ± 0.001

10,000

Methods	AbsRel ↓	RMSE ↓	log10 ↓	δ↑
Baseline	0.112 ± 0.004	0.389 ± 0.006	0.057 ± 0.003	0.893 ± 0.003
Ours	0.107 ± 0.002	0.386 ± 0.006	0.054 ± 0.002	0.896 ± 0.002

1,000

Methods	AbsRel ↓	RMSE ↓	log10 ↓	δ↑
Baseline	0.141 ± 0.008	0.447 ± 0.009	0.066 ± 0.004	0.843 ± 0.006
Ours	0.135 ± 0.007	0.440 ± 0.008	0.065 ± 0.004	0.853 ± 0.005

100

Methods	AbsRel ↓	RMSE ↓	log10 ↓	δ↑
Baseline	0.199 ± 0.011	0.604 ± 0.014	0.086 ± 0.005	0.694 ± 0.011
Ours	0.182 ± 0.008	0.594 ± 0.012	0.083 ± 0.003	0.718 ± 0.010

10

Methods	AbsRel ↓	RMSE ↓	log10 ↓	δ↑
Baseline	0.321 ± 0.040	0.872 ± 0.042	0.124 ± 0.008	0.523 ± 0.027
Ours	0.292 ± 0.031	0.814 ± 0.037	0.112 ± 0.006	0.561 ± 0.021

Qualitative results on the KITTI dataset. (a) RGB image, predicted depth maps by (b), (d) baseline, and (c), (e) ours using 100 and 10,000 labeled frames, respectively.

Qualitative results on the NYU-Depth-v2 dataset. (a) RGB image, (b) ground-truth depth map, and predicted depth maps by (c), (e) baseline, and (d), (f) ours using 100 and 10,000 labeled frames, respectively.

Citation

Please consider citing our paper if you use this code.

@article{baek2022semi,
  title={Semi-Supervised Learning of Monocular Depth Estimation via Consistency Regularization with K-way Disjoint Masking},
  author={Baek, Jongbeom and Kim, Gyeongnyeon and Park, Seonghoon and An, Honggyu and Poggi, Matteo and Kim, Seungryong},
  journal={arXiv preprint arXiv:2212.10806},
  year={2022}
}