# Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review

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## Abstract

**:**

## 1. Introduction

## 2. An Overview of Monocular Depth Estimation

#### 2.1. Problem Representation

#### 2.2. Traditional Methods for Depth Estimation

#### 2.3. Datasets for Depth Estimation

**NYU-v2:**the NYU-v2 dataset for depth estimation was introduced in [29]. The dataset consists of 1449 RGB images densely labelled with depth images. The datasets consist of 407K frames of 464 scenes taken from three different cities. These datasets are used for indoor scenes depth estimation, segmentation and classification.**Make3D:**the Make3D dataset, introduced in [30], contains 400 and 134 outdoor images for training and testing, respectively. This dataset contains different types of outdoor, indoor and synthetic scenes that are used for depth estimation by presenting a more complex set of features.**KITTI:**the KITTI dataset, introduced in [31], has two versions and is made of 394 road scenes providing RGB stereo sets and corresponding ground truth depth maps. The KITTI dataset is further divided into RD: KITTI Raw Depth [31]; CD: KITTI Continuous Depth [31,32]; SD: KITTI Semi-Dense Depth [31,32]; ES: Eigen Split [33]; ID: KITTI Improved Depth [34]. KITTI datasets are commonly used for different tasks including 3D object detection and depth estimation. The high-quality ground truth images are captured using the Velodyne laser scanner.**Pandora:**the Pandora dataset, introduced [35], contains 250K full resolution RGB and corresponding depth images having their corresponding annotation. Pandora dataset is used for head centre localization, head pose estimation and shoulder pose estimation.**SceneFlow:**this was introduced in [36] as one of the very first large-scale synthetic datasets consist of 39K stereo images with corresponding disparity, depth, optical flow and segmentation masks.

## 3. Deep Learning and Monocular Depth Estimation

#### 3.1. Supervised Methods

#### 3.2. Self-Supervised Methods

#### 3.3. Semi-Supervised Methods

## 4. Evaluation Matrices and Criteria

## 5. Discussion

#### 5.1. Comparison Analysis Based on Performance

**I. Degree of supervision:**most of the methods demonstrated in this paper require ground truth depth images for training. These supervised methods perform well and most of them are state-of-the-art on common benchmarks. Methods such as DeepV2D [50], BTS [49] and VNL [48] showed a much faster performance time compared to the other models. On the other hand, VNL [48], ACAN [46] and EMDEOM [32] provides the depth information with much lower resolution compared to the state-of-the-art. Unlike VNL [48], DORN [18] has the highest number of parameters in the supervised category and it requires a high number of operations making it an inefficient choice for real-life applications.

**II. Accuracy and depth range:**based on our evaluations, DeepV2D [50] marginally achieved the best performance compared to BTS [49] and the rest of the methods. On KITTI [31] dataset the model achieved 2.005 RMSE and threshold accuracy of 0.977 with $\delta <{1.25}^{3}$. On NYUD-v2 [29] dataset it achieved 0.403 RMSE and threshold accuracy of 0.996 with $\delta <{1.25}^{3}$. As shown in Table 4 and Table 5, methods with 3D geometry constraint or features, outperform the others, which shows the importance of high order 3D geometric constraints for depth estimation.

**III. Computation time and memory:**based on the comparisons presented in Table 3, Table 4, Table 5 and Table 6, VNL [48] significantly reduced the computational time and memory footprint, which can be used for both quality and low-cost monocular depth estimation.

#### 5.2. Future Research Directions

- Complex deep networks are very expansive in terms of memory requirements, which is a major issue when dealing with high-resolution images and when aiming to predict high-resolution depth images.
- Developments in high-performance computing can address the memory and computational issues, however, devolving lighter deep network architectures remains desirable especially if it is to be deployed in smart consumer devices.
- Another challenge is how to achieve higher accuracy, in general, which is affected by the complex scenarios, such as occlusions, highly cluttered scenes and complex material properties of the objects.
- Deep-learning methods rely heavily on the training datasets annotated with ground truth labels for depth estimation which is very expansive to obtain in the real world.
- We expect in the future to see the emergence of large databases for 3D reconstruction. Emerging new self-adoption methods that can adapt themselves to new circumstances in real-time or with minimum supervision are one of the promising future directions for research in depth estimation.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

#### Low-Performance Monocular Depth Estimation Methods

**Table A1.**Properties of the low-accuracy methods trained on either KITTI or NYU-v2 datasets. (FC: fully convolutional, ED: encoder-decoder, AD: auto-decoder, K: trained on KITTI dataset, N: trained on NYU-v2 dataset and CNN: convolutional neural networks).

Method | Input | Type | Optimizer | Parameters | Output | GPU Memory | RMSE | GPU Model |
---|---|---|---|---|---|---|---|---|

Zhou et al. [70] | $128\times $ 416 K | CNN | Adam | N/A | $128\times $ 416 K | N/A | 4.975 | N/A |

Casser et al. [73] | $128$ × 416 K | CNN | Adam | N/A | $128\times $ 416 K | 11 GB | 4.7503 | 1080 Ti |

Guizilini et al. [74] | $640\times $ 192 K | FC | Adam | 86M | $640$ × 192 K | N/A | 4.601 | N/A |

Godard et al. [15] | $640\times $ 192 K | FC | Adam | 31M | $640$ × 192 K | 12 GB | 4.935 | TITAN Xp |

Eigen et al. [33] | $640\times $ 184 K | CNN | Adam | N/A | $640$ × 184 | 6 GB | N/A | TITAN Black |

Guizilin et al. [75] | $640\times $ 192 K | ED | Adam | 79M | $640$ × 192 | $8$ × 16 GB | 4.270 | Tesla V100 |

Tang et al. [76] | $640\times $ 192 K | CNN | RMSprop | 80M | $640\times $ 192 | 12 GB | N/A | N/A |

Ramamonjisoa et al. [40] | $640\times $ 480 N | ED | Adam | 69M | $640\times $ 480 N | 11 GB | 0.401 | 1080 Ti |

Riegler et al. [39] | N/A | ED | Adam | N/A | N/A | N/A | N/A | N/A |

Ji et al. [37] | $320\times $ 240 N | ED | Adam | N/A | $320\times $ 240 N | 12 GB | 0.704 | TITAN Xp |

Almalioglu et al. [77] | $128\times $ 416 K | GAN | RMSprop | 63M | $128\times $ 416 K | 12 GB | 5.448 | TITAN V |

Pillai et al. [41] | $128\times $ 416 K | CNN | Adam | 97M | $128\times $ 416 K | $8$ × 16 GB | 4.958 | Tesla V100 |

Wofk et al. [24] | $224\times $ 224 N | ED | SGD | N/A | $224\times $ 224 N | N/A | 0.604 | N/A |

Watson et al. [78] | $128\times $ 416 K | ED | SGD | N/A | $128\times $ 416 K | N/A | N/A | N/A |

Chen et al. [79] | $256$ × 512 K | ED | Adam | N/A | $256\times $ 512 K | 11 GB | 3.871 | 1080 Ti |

Lee et al. [80] | $640\times $ 480 N | CNN | SGD | 61M | $640\times $ 480 N | N/A | 0.538 | N/A |

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Dataset | Labelled Images | Annotation | Brief Description |
---|---|---|---|

NYU-v2 [29] | 1449 | Depth + Segmentation | Red-green-blue (RGB) and depth images taken from indoor scenes. |

Make3D [30] | 534 | Depth | RGB and depth images taken from outdoor scenes. |

KITTI [31] | 94K | Depth aligned with RAW data + Optical Flow | RGB and depth from 394 road scenes. |

Pandora [35] | 250K | Depth + Annotation | RGB and depth images. |

SceneFlow [36] | 39K | Depth + Disparity + Optical Flow+ Segmentation Map | Stereo image sets rendered from synthetic data with ground truth depth, disparity and optical flow. |

**Table 2.**Categories of deep learning-based monocular depth estimation methods (FC: fully convolutional; CNN: convolutional neural networks).

Method | Architecture | Category |
---|---|---|

EMDEOM [32] | FC | Supervised |

ACAN [46] | Encoder-Decoder | |

DenseDepth [47] | Encoder-Decoder | |

DORN [18] | CNN | |

VNL [48] | Encoder-Decoder | |

BTS [49] DeepV2D [50] | Encoder-Decoder CNN | |

LISM [51] | Encoder-Decoder | Self-supervised |

monoResMatch [38] | CNN | |

PackNet-SfM [52] | CNN | |

VOMonodepth [53] | Auto-Decoder | |

monodepth2 [42] | CNN | |

GASDA [54] | CNN | Semi-supervised |

**Table 3.**Properties of the studied methods for monocular depth estimation (FC: fully convolutional; ED: encoder-decoder; AD: auto-decoder; CNN: convolutional neural networks; K: trained on KITTI; N: trained on NYU-v2).

Method | Input | Type | Optimizer | Parameters | Output | GPU Memory | GPU Model |
---|---|---|---|---|---|---|---|

BTS [49] | $352\times $ 704 K | ED | Adam | 47M | $352\times $ 704 K | $4\times $11 GB | 1080 Ti |

DORN [18] | $385\times $513 K | CNN | Adam | 123.4M | $513$ × 385 K | 12 GB | TITAN Xp |

VNL [48] | $384\times $ 384 N | ED | SGD | 2.7M | $384$ × 384 N | N/A | N/A |

ACAN [46] | $256\times $ 352 N | ED | SGD | 80M | $256$ × 352 N | 11 GB | 1080 Ti |

VOMonodepth [53] | $256$ × 512 K | AD | Adam | 35M | $256\times $512 K | 12 GB | TITAN Xp |

LSIM [51] | $1242$ × 375 K | ED | Adam | 73.3M | $1242$ × 375 K | 12 GB | TITAN Xp |

GASDA [54] | $192$ × 640 K | CNN | Adam | 70M | $192$ × 640 K | N/A | N/A |

DenseDepth [47] | $640$ × 480 N | ED | Adam | 42.6M | $320$ × 240 N | $4\times $12 GB | TITAN Xp |

monoResMatch [38] | $192$ × 640 K | CNN | Adam | 42.5M | $192$ × 640 K | 12 GB | TITAN Xp |

EMDEOM [32] | $304$ × 228 K | FC | Adam | 63M | $128$ × 160 K | 12 GB | TITAN Xp |

PackNet-SfM [52] | $640$ × 192 K | CNN | Adam | 128M | $640$ × 192 K | $8\times $16 GB | Tesla V100 |

monodepth2 [42] DeepV2D [50] | $640\times $ 192 K $640$ × 480 N | CNN CNN | Adam RMSProp | 70M 32M | $640$ × 192 K $640$ × 480 N | 12 GB 11 GB | TITAN Xp 1080 Ti |

Method | Train | Test | Abs Rel | Sq Rel | RMSE | RMSElog | $\mathit{\delta}<1.25$ | $\mathit{\delta}<{1.25}^{2}$ | $\mathit{\delta}<{1.25}^{3}$ |
---|---|---|---|---|---|---|---|---|---|

BTS [49] | ES(RD) | ES(RD) | 0.060 | 0.182 | 2.005 | 0.092 | 0.959 | 0.994 | 0.999 |

DORN [18] | ES(RD) | ES(RD) | 0.071 | 0.268 | 2.271 | 0.116 | 0.936 | 0.985 | 0.995 |

VNL [48] | ES(RD) | ES(RD) | 0.072 | 0.883 | 3.258 | 0.117 | 0.938 | 0.990 | 0.998 |

ACAN [46] | ES(RD) | ES(RD) | 0.083 | 0.437 | 3.599 | 0.127 | 0.919 | 0.982 | 0.995 |

VOMonodepth [53] | ES(RD) | ES(RD) | 0.091 | 0.548 | 3.790 | 0.181 | 0.892 | 0.956 | 0.979 |

LSIM [51] | FT | RD | 0.169 | 0.6531 | 3.790 | 0.195 | 0.867 | 0.954 | 0.979 |

GASDA [54] | ES(RD) | ES(RD) | 0.143 | 0.756 | 3.846 | 0.217 | 0.836 | 0.946 | 0.976 |

DenseDepth [47] | ES(RD) | ES(RD) | 0.093 | 0.589 | 4.170 | 0.171 | 0.886 | 0.965 | 0.986 |

monoResMatch [38] | ES(RD) | ES(RD) | 0.096 | 0.673 | 4.351 | 0.184 | 0.890 | 0.961 | 0.981 |

EMDEOM [32] | RD, CD | SD | 0.118 | 0.630 | 4.520 | 0.209 | 0.898 | 0.966 | 0.985 |

monodepth2 [42] | ES(RD) | ES(RD) | 0.115 | 0.903 | 4.863 | 0.193 | 0.877 | 0.959 | 0.981 |

PackNet-SfM [52] | ES(RD) | ID | 0.078 | 0.420 | 3.485 | 0.121 | 0.931 | 0.986 | 0.996 |

DeepV2D [50] | ES(RD) | ES(RD) | 0.037 | 0.174 | 2.005 | 0.074 | 0.977 | 0.993 | 0.997 |

**Table 5.**Evaluation results on NYU-v2 dataset. Best method per metric is emboldened and highlighted in green.

Method | Abs Rel | Sq Rel | RMSE | RMSElog | $\mathit{\delta}<1.25$ | $\mathit{\delta}<{1.25}^{2}$ | $\mathit{\delta}<{1.25}^{3}$ |
---|---|---|---|---|---|---|---|

BTS [49] | 0.112 | 0.025 | 0.352 | 0.047 | 0.882 | 0.979 | 0.995 |

VNL [48] | 0.113 | 0.034 | 0.364 | 0.054 | 0.815 | 0.990 | 0.993 |

DenseDepth [47] | 0.123 | 0.045 | 0.465 | 0.053 | 0.846 | 0.970 | 0.994 |

ACAN [46] | 0.123 | 0.101 | 0.496 | 0.174 | 0.826 | 0.974 | 0.990 |

DORN [18] | 0.138 | 0.051 | 0.509 | 0.653 | 0.825 | 0.964 | 0.992 |

monoResMatch [38] | 1.356 | 1.156 | 0.694 | 1.125 | 0.825 | 0.965 | 0.967 |

monodepth2 [42] | 2.344 | 1.365 | 0.734 | 1.134 | 0.826 | 0.958 | 0.979 |

EMDEOM [32] | 2.035 | 1.630 | 0.620 | 1.209 | 0.896 | 0.957 | 0.984 |

LSIM [51] | 2.344 | 1.156 | 0.835 | 1.175 | 0.815 | 0.943 | 0.975 |

PackNet-SfM [52] | 2.343 | 1.158 | 0.887 | 1.234 | 0.821 | 0.945 | 0.968 |

GASDA [54] | 1.356 | 1.156 | 0.963 | 1.223 | 0.765 | 0.897 | 0.968 |

VOMonodepth [53] | 2.456 | 1.192 | 0.985 | 1.234 | 0.756 | 0.884 | 0.965 |

DeepV2D [50] | 0.061 | 0.094 | 0.403 | 0.026 | 0.956 | 0.989 | 0.996 |

**Table 6.**Comparison of the models in terms of inference time (FC: fully convolutional; CNN: convolutional neural networks). Best method is emboldened and highlighted in green.

Method | Inference Time | Network/FC/CNN |
---|---|---|

BTS [49] | 0.22 s | Encoder-decoder |

VNL [48] | 0.25 s | Auto-decoder |

DeepV2D [50] | 0.36 s | CNN |

ACAN [46] | 0.89 s | Encoder-decoder |

VOMonodepth [53] | 0.34 s | CNN |

LSIM [51] | 0.54 s | CNN |

GASDA [54] | 0.57 s | Encoder-decoder |

DenseDepth [47] | 0.35 s | Encoder-decoder |

monoResMatch [38] | 0.37 s | CNN |

EMDEOM [32] | 0.63 s | FC |

DORN [18] | 0.98 s | Encoder-decoder |

PackNet-SfM [52] | 0.97 s | CNN |

monodepth2 [42] | 0.56 s | CNN |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Khan, F.; Salahuddin, S.; Javidnia, H. Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review. *Sensors* **2020**, *20*, 2272.
https://doi.org/10.3390/s20082272

**AMA Style**

Khan F, Salahuddin S, Javidnia H. Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review. *Sensors*. 2020; 20(8):2272.
https://doi.org/10.3390/s20082272

**Chicago/Turabian Style**

Khan, Faisal, Saqib Salahuddin, and Hossein Javidnia. 2020. "Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review" *Sensors* 20, no. 8: 2272.
https://doi.org/10.3390/s20082272