scholarly journals EfficientNet-B0 Based Monocular Dense-Depth Map Estimation

2021 ◽  
Vol 38 (5) ◽  
pp. 1485-1493
Author(s):  
Yasasvy Tadepalli ◽  
Meenakshi Kollati ◽  
Swaraja Kuraparthi ◽  
Padmavathi Kora
Keyword(s):  
Depth Map ◽  
Depth Estimation ◽  
Model Parameters ◽  
Map Estimation ◽  
Bilinear Method ◽  
Regression Problem ◽  
Actual Error ◽  
Ill Posed ◽  
Monocular Image ◽  
Monocular Depth

Monocular depth estimation is a hot research topic in autonomous car driving. Deep convolution neural networks (DCNN) comprising encoder and decoder with transfer learning are exploited in the proposed work for monocular depth map estimation of two-dimensional images. Extracted CNN features from initial stages are later upsampled using a sequence of Bilinear UpSampling and convolution layers to reconstruct the depth map. The encoder forms the feature extraction part, and the decoder forms the image reconstruction part. EfficientNetB0, a new architecture is used with pretrained weights as encoder. It is a revolutionary architecture with smaller model parameters yet achieving higher efficiencies than the architectures of state-of-the-art, pretrained networks. EfficientNet-B0 is compared with two other pretrained networks, the DenseNet-121 and ResNet50 models. Each of these three models are used in encoding stage for features extraction followed by bilinear method of UpSampling in the decoder. The Monocular image is an ill-posed problem and is thus considered as a regression problem. So the metrics used in the proposed work are F1-score, Jaccard score and Mean Actual Error (MAE) etc., between the original and the reconstructed image. The results convey that EfficientNet-B0 outperforms in validation loss, F1-score and Jaccard score compared to DenseNet-121 and ResNet-50 models.

scholarly journals CNN Based Monocular Depth Estimation

2021 ◽  
Vol 309 ◽  
pp. 01070
Author(s):  
K. Swaraja ◽  
K. Naga Siva Pavan ◽  
S. Suryakanth Reddy ◽  
K. Ajay ◽  
P. Uday Kiran Reddy ◽  
...  
Keyword(s):  
High Resolution ◽  
Depth Map ◽  
Depth Estimation ◽  
Learning Approaches ◽  
Depth Estimate ◽  
High Performing ◽  
Depth Measurement ◽  
Ill Posed ◽  
Monocular Depth ◽  
Public Datasets

In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine the errors created by various models in order to expose the shortcomings of present approaches which accomplishes viable performance on KITTI besides NYU Depth v2.

scholarly journals Efficient Monocular Depth Estimation with Transfer Feature Enhancement

2021 ◽  
Vol 15 ◽  
pp. 1165-1173
Author(s):  
Ming Yin
Keyword(s):  
State Of The Art ◽  
Depth Estimation ◽  
Kernel Size ◽  
Training Time ◽  
Lower Error Rate ◽  
Proposed Model ◽  
Ill Posed ◽  
Monocular Image ◽  
Monocular Depth ◽  
End Depth

Estimating the depth of the scene from a monocular image is an essential step for image semantic understanding. Practically, some existing methods for this highly ill-posed issue are still in lack of robustness and efficiency. This paper proposes a novel end-to-end depth esti- mation model with skip connections from a pre- trained Xception model for dense feature extrac- tion, and three new modules are designed to im- prove the upsampling process. In addition, ELU activation and convolutions with smaller kernel size are added to improve the pixel-wise regres- sion process. The experimental results show that our model has fewer network parameters, a lower error rate than the most advanced networks and requires only half the training time. The evalu- ation is based on the NYU v2 dataset, and our proposed model can achieve clearer boundary de- tails with state-of-the-art effects and robustness.

scholarly journals Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 54
Author(s):  
Peng Liu ◽  
Zonghua Zhang ◽  
Zhaozong Meng ◽  
Nan Gao
Keyword(s):  
Joint Attention ◽  
Loss Function ◽  
Depth Estimation ◽  
Depth Information ◽  
3D Vision ◽  
Network Training ◽  
Crucial Component ◽  
Benchmark Datasets ◽  
Ill Posed ◽  
Monocular Depth

Depth estimation is a crucial component in many 3D vision applications. Monocular depth estimation is gaining increasing interest due to flexible use and extremely low system requirements, but inherently ill-posed and ambiguous characteristics still cause unsatisfactory estimation results. This paper proposes a new deep convolutional neural network for monocular depth estimation. The network applies joint attention feature distillation and wavelet-based loss function to recover the depth information of a scene. Two improvements were achieved, compared with previous methods. First, we combined feature distillation and joint attention mechanisms to boost feature modulation discrimination. The network extracts hierarchical features using a progressive feature distillation and refinement strategy and aggregates features using a joint attention operation. Second, we adopted a wavelet-based loss function for network training, which improves loss function effectiveness by obtaining more structural details. The experimental results on challenging indoor and outdoor benchmark datasets verified the proposed method’s superiority compared with current state-of-the-art methods.

scholarly journals Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xin Yang ◽  
Qingling Chang ◽  
Xinglin Liu ◽  
Siyuan He ◽  
Yan Cui
Keyword(s):  
Depth Map ◽  
Depth Estimation ◽  
Multi Scale ◽  
Monocular Depth

scholarly journals The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  
Keyword(s):  
Structural Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Image Synthesis ◽  
Map Estimation ◽  
Estimation Model ◽  
Depth Prediction ◽  
Underwater Image ◽  
Underwater Vision ◽  
Image Depth

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.

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

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2272 ◽  
Author(s):  
Faisal Khan ◽  
Saqib Salahuddin ◽  
Hossein Javidnia
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Research Work ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Future Research ◽  
Comprehensive Overview ◽  
Ill Posed ◽  
Monocular Depth

Monocular depth estimation from Red-Green-Blue (RGB) images is a well-studied ill-posed problem in computer vision which has been investigated intensively over the past decade using Deep Learning (DL) approaches. The recent approaches for monocular depth estimation mostly rely on Convolutional Neural Networks (CNN). Estimating depth from two-dimensional images plays an important role in various applications including scene reconstruction, 3D object-detection, robotics and autonomous driving. This survey provides a comprehensive overview of this research topic including the problem representation and a short description of traditional methods for depth estimation. Relevant datasets and 13 state-of-the-art deep learning-based approaches for monocular depth estimation are reviewed, evaluated and discussed. We conclude this paper with a perspective towards future research work requiring further investigation in monocular depth estimation challenges.

scholarly journals BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation

2021 ◽  
Author(s):  
Qi Tang ◽  
Runmin Cong ◽  
Ronghui Sheng ◽  
Lingzhi He ◽  
Dan Zhang ◽  
...  
Keyword(s):  
Super Resolution ◽  
Depth Map ◽  
Depth Estimation ◽  
Joint Learning ◽  
Learning Network ◽  
Monocular Depth

scholarly journals Merging Static and Dynamic Depth Cues with Optical-Flow Recovery for Creating Stereo Videos

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Fang-Hsuan Cheng ◽  
Tze-Yun Sung
Keyword(s):  
Optical Flow ◽  
Estimation Method ◽  
Depth Map ◽  
Depth Estimation ◽  
Depth Information ◽  
Additional Information ◽  
Optical Flow Method ◽  
Block Motion ◽  
Monocular Image ◽  
Monocular Image Sequence

A method for estimating the depth information of a general monocular image sequence and then creating a 3D stereo video is proposed. Distinguishing between foreground and background is possible without additional information, and then foreground pixels are moved to create the binocular image. The proposed depth estimation method is based on coarse-to-fine strategy. By applying the CID method in the spatial domain, the sharpness and the contrast of an image can be improved by the distance of the region based on its color. Then a coarse depth map of the image can be generated. An optical-flow method based on temporal information is then used to search and compare the block motion status between previous and current frames, and then the distance of the block can be estimated according to the amount of block motion. Finally, the static and motion depth information is integrated to create the fine depth map. By shifting foreground pixels based on the depth information, a binocular image pair can be created. A sense of 3D stereo can be obtained without glasses by an autostereoscopic 3D display.

scholarly journals Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1179 ◽  
Author(s):  
Tao Huang ◽  
Shuanfeng Zhao ◽  
Longlong Geng ◽  
Qian Xu
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Depth Map ◽  
Ground Truth ◽  
Depth Estimation ◽  
Input Image ◽  
Superior Performance ◽  
Estimation Methods ◽  
Depth Information ◽  
Monocular Depth

To take full advantage of the information of images captured by drones and given that most existing monocular depth estimation methods based on supervised learning require vast quantities of corresponding ground truth depth data for training, the model of unsupervised monocular depth estimation based on residual neural network of coarse–refined feature extractions for drone is therefore proposed. As a virtual camera is introduced through a deep residual convolution neural network based on coarse–refined feature extractions inspired by the principle of binocular depth estimation, the unsupervised monocular depth estimation has become an image reconstruction problem. To improve the performance of our model for monocular depth estimation, the following innovations are proposed. First, the pyramid processing for input image is proposed to build the topological relationship between the resolution of input image and the depth of input image, which can improve the sensitivity of depth information from a single image and reduce the impact of input image resolution on depth estimation. Second, the residual neural network of coarse–refined feature extractions for corresponding image reconstruction is designed to improve the accuracy of feature extraction and solve the contradiction between the calculation time and the numbers of network layers. In addition, to predict high detail output depth maps, the long skip connections between corresponding layers in the neural network of coarse feature extractions and deconvolution neural network of refined feature extractions are designed. Third, the loss of corresponding image reconstruction based on the structural similarity index (SSIM), the loss of approximate disparity smoothness and the loss of depth map are united as a novel training loss to better train our model. The experimental results show that our model has superior performance on the KITTI dataset composed by corresponding left view and right view and Make3D dataset composed by image and corresponding ground truth depth map compared to the state-of-the-art monocular depth estimation methods and basically meet the requirements for depth information of images captured by drones when our model is trained on KITTI.

scholarly journals Monocular Depth Estimation using Transfer learning-An Overview

2021 ◽  
Vol 309 ◽  
pp. 01069
Author(s):  
K. Swaraja ◽  
V. Akshitha ◽  
K. Pranav ◽  
B. Vyshnavi ◽  
V. Sai Akhil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Deep Neural Networks ◽  
Depth Estimation ◽  
Depth Information ◽  
Learning Approaches ◽  
Learning Network ◽  
Depth Maps ◽  
Ill Posed ◽  
Monocular Depth

Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.

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