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Journal articles on the topic 'Depth estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Choi, Youn-Ho, and Seok-Cheol Kee. "Monocular Depth Estimation Using a Laplacian Image Pyramid with Local Planar Guidance Layers." Sensors 23, no. 2 (2023): 845. http://dx.doi.org/10.3390/s23020845.

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It is important to estimate the exact depth from 2D images, and many studies have been conducted for a long period of time to solve depth estimation problems. Recently, as research on estimating depth from monocular camera images based on deep learning is progressing, research for estimating accurate depths using various techniques is being conducted. However, depth estimation from 2D images has been a problem in predicting the boundary between objects. In this paper, we aim to predict sophisticated depths by emphasizing the precise boundaries between objects. We propose a depth estimation net

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2

Fan, Junkai, Kun Wang, Zhiqiang Yan, et al. "Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2852–60. https://doi.org/10.1609/aaai.v39i3.32291.

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In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enhanced depth estimation improves dehazing via the atmospheric scattering model (ASM), while superior dehazing contributes to more accurate depth estimation through the brightness consistency constraint (BCC). To tackle these intertwined tasks, we propose a novel depth-centric learning framework that integrates the ASM model with the BCC constraint. Our key idea is that both ASM and BCC rely on a shared depth estimation

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Salokhiddinov, Sherzod, and Seungkyu Lee. "Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus." Applied Sciences 10, no. 23 (2020): 8522. http://dx.doi.org/10.3390/app10238522.

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Estimating the 3D shape of a scene from differently focused set of images has been a practical approach for 3D reconstruction with color cameras. However, reconstructed depth with existing depth from focus (DFF) methods still suffer from poor quality with textureless and object boundary regions. In this paper, we propose an improved depth estimation based on depth from focus iteratively refining 3D shape from uniformly focused image set (UFIS). We investigated the appearance changes in spatial and frequency domains in iterative manner. In order to achieve sub-frame accuracy in depth estimation

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4

O. Shim, Seong. "Depth Estimation Based on 3D Focus Measurement." International Journal of Modeling and Optimization 5, no. 4 (2015): 273–76. http://dx.doi.org/10.7763/ijmo.2015.v5.473.

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5

Lee, Jae-Han, and Chang-Su Kim. "Single-image depth estimation using relative depths." Journal of Visual Communication and Image Representation 84 (April 2022): 103459. http://dx.doi.org/10.1016/j.jvcir.2022.103459.

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6

Shen, Linglin, and Xiangbo Sun. "Source Depth Estimation Based on Random Forest Approach Using Ocean Waveguide Data." Journal of Research in Science and Engineering 7, no. 1 (2025): 96–99. https://doi.org/10.53469/jrse.2025.07(01).15.

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In practice, the estimation of source localization based on matched field processing is significantly affected by environmental parameters, leading to the so-called mismatch problem. This paper models the sound source depth estimation problem as a classification issue in machine learning and discusses how the random forest method can be used to solve the depth estimation problem of sound sources. The paper uses the SWELLEX-96 sea trial environmental parameters and the Kraken model to generate ocean waveguide data received by a vertical line array at different depths of the sound source. After

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Swaraja, K., V. Akshitha, K. Pranav, et al. "Monocular Depth Estimation using Transfer learning-An Overview." E3S Web of Conferences 309 (2021): 01069. http://dx.doi.org/10.1051/e3sconf/202130901069.

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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

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Wilson, Greg, and H. Tuba Özkan-Haller. "Ensemble-Based Data Assimilation for Estimation of River Depths." Journal of Atmospheric and Oceanic Technology 29, no. 10 (2012): 1558–68. http://dx.doi.org/10.1175/jtech-d-12-00014.1.

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Abstract A method is presented for estimating bathymetry in a river, based on observations of depth-averaged velocity during steady flow. The estimator minimizes a cost function that combines known information in the form of a prior estimate and measured data (including measurement noise). State augmentation is used to relate the measured variable (velocity) to the unknown parameter (bathymetry). Specifically, the unknown consists of deviations in depth about a known along-channel mean. Verification of the method is performed using a simple 1D channel geometry as well as for two real-world rea

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9

Yoo, Jisang, Woomin Jun, and Sungjin Lee. "Research on Monocular Depth Estimation for Autonomous Driving." Journal of Korean Institute of Communications and Information Sciences 48, no. 12 (2023): 1623–32. http://dx.doi.org/10.7840/kics.2023.48.12.1623.

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10

Vo, Minh-Quan, Thu Nguyen, Michael A. Riegler, and Hugo L. Hammer. "Efficient Estimation of Generative Models Using Tukey Depth." Algorithms 17, no. 3 (2024): 120. http://dx.doi.org/10.3390/a17030120.

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Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of the models challenging. The most commonly used alternative strategy is called likelihood-free estimation, based on finding values of the model parameters such that a set of selected statistics have similar values in the dataset and in samples generated from the model. However, a challenge is how to select statistics that are efficient in estimating unknown parameters. The most co

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11

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

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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 representati

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12

Tan, Daniel Stanley, Chih-Yuan Yao, Conrado Ruiz, and Kai-Lung Hua. "Single-Image Depth Inference Using Generative Adversarial Networks." Sensors 19, no. 7 (2019): 1708. http://dx.doi.org/10.3390/s19071708.

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Depth has been a valuable piece of information for perception tasks such as robot grasping, obstacle avoidance, and navigation, which are essential tasks for developing smart homes and smart cities. However, not all applications have the luxury of using depth sensors or multiple cameras to obtain depth information. In this paper, we tackle the problem of estimating the per-pixel depths from a single image. Inspired by the recent works on generative neural network models, we formulate the task of depth estimation as a generative task where we synthesize an image of the depth map from a single R

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13

Saura-Herreros, Miguel, Angeles Lopez, and Jose Ribelles. "Spherical panorama compositing through depth estimation." Visual Computer 37, no. 9-11 (2021): 2809–21. http://dx.doi.org/10.1007/s00371-021-02239-7.

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AbstractIn this paper, we propose to work in the 2.5D space of the scene to facilitate composition of new spherical panoramas. For adding depths to spherical panoramas, we extend an existing method that was designed to estimate relative depths from a single perspective image through user interaction. We analyze the difficulties to interactively provide such depth information for spherical panoramas, through three different types of presentation. Then, we propose a set of basic tools to interactively manage the relative depths of the panoramas in order to obtain a composition in a very simple w

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14

Yun, Ilwi, Hyuk-Jae Lee, and Chae Eun Rhee. "Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3224–33. http://dx.doi.org/10.1609/aaai.v36i3.20231.

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Due to difficulties in acquiring ground truth depth of equirectangular (360) images, the quality and quantity of equirectangular depth data today is insufficient to represent the various scenes in the world. Therefore, 360 depth estimation studies, which relied solely on supervised learning, are destined to produce unsatisfactory results. Although self-supervised learning methods focusing on equirectangular images (EIs) are introduced, they often have incorrect or non-unique solutions, causing unstable performance. In this paper, we propose 360 monocular depth estimation methods which improve

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15

Jun, Jinyoung, Jae-Han Lee, Chul Lee, and Chang-Su Kim. "Monocular Human Depth Estimation Via Pose Estimation." IEEE Access 9 (2021): 151444–57. http://dx.doi.org/10.1109/access.2021.3126629.

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16

Parlewar, Pallavi. "Depth Estimation using FCNN." Bioscience Biotechnology Research Communications 13, no. 14 (2020): 372–75. http://dx.doi.org/10.21786/bbrc/13.14/86.

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17

Pan, Janice, and Alan C. Bovik. "Perceptual Monocular Depth Estimation." Neural Processing Letters 53, no. 2 (2021): 1205–28. http://dx.doi.org/10.1007/s11063-021-10437-6.

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18

Kimura, M., and E. Satoh. "Estimation of Well Depth." Progress of Theoretical Physics 91, no. 2 (1994): 319–26. http://dx.doi.org/10.1143/ptp/91.2.319.

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19

Ludwig, Kai-Oliver, Heiko Neumann, and Bernd Neumann. "Local stereoscopic depth estimation." Image and Vision Computing 12, no. 1 (1994): 16–35. http://dx.doi.org/10.1016/0262-8856(94)90052-3.

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20

Fitian R. Al-Rawi and Sabbah J. D. Shejir. "Comparison of Percentage Error in Depth Estimation of Magnetic Anomalies Due Dyke-like Bodies." Jornual of AL-Farabi for Engineering Sciences 1, no. 1 (2019): 4. http://dx.doi.org/10.59746/jfes.v1i1.51.

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The Percentage errors in depth estimation from magnetic anomalies due to dyke-like bodies, having various depths and inclination angles, are calculated. Two methods have been used to estimate the depth of these bodies and then their percentage errors in depth estimation are compared. These methods are the well-known slope-half slope method and the method adopting procedure through using Fraser filter. The two methods have accompanied various percent errors in depth estimation for models having various magnetic parameters. The comparison between the calculated error values obtained from the two

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Cipin, Radoslav, Marek Toman, Petr Prochazka, and Ivo Pazdera. "Estimation of Depth of Discharge of Li-ion Battery Using Neural Network." ECS Transactions 105, no. 1 (2021): 541–47. http://dx.doi.org/10.1149/10501.0541ecst.

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This paper deals with the estimation of depth of discharge for Li-ion batteries. Estimation is based on the knowledge of discharging curves measured for discrete values of loading currents. The estimator of the depth of discharge is a form of feedforward neural network which is trained with the measured data of discharge curves. Accuracy of estimation of the depth of discharge is shown for arbitrary generated and measured loading characteristics, where the depth of discharge is estimated by the designed neural network and measured by using the Coulomb counting method.

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22

Priya, R. Arokia, and Shreedhar Gyandeo Pawar. "Parallel Algorithm Using Opencl for Depth Estimation of Image." International Journal of Scientific Research 2, no. 10 (2012): 1–5. http://dx.doi.org/10.15373/22778179/oct2013/46.

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23

Gao, Ming, Huiping Deng, Sen Xiang, Jin Wu, and Zeyang He. "EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism." Sensors 22, no. 16 (2022): 6291. http://dx.doi.org/10.3390/s22166291.

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Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency—in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EP

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Arahal, Manuel R., Alfredo Pérez Vega-Leal, Manuel G. Satué, and Sergio Esteban. "Assessing SOC Estimations via Reverse-Time Kalman for Small Unmanned Aircraft." Energies 17, no. 20 (2024): 5161. http://dx.doi.org/10.3390/en17205161.

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This paper presents a method to validate state of charge (SOC) estimations in batteries for their use in remotely manned aerial vehicles (UAVs). The SOC estimation must provide the mission control with a measure of the available range of the aircraft, which is critical for extended missions such as search and rescue operations. However, the uncertainty about the initial state and depth of discharge during the mission makes the estimation challenging. In order to assess the estimation provided to mission control, an a posteriori re-estimation is performed. This allows for the assessment of esti

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Yu, Kegen, Yunwei Li, Taoyong Jin, Xin Chang, Qi Wang, and Jiancheng Li. "GNSS-R-Based Snow Water Equivalent Estimation with Empirical Modeling and Enhanced SNR-Based Snow Depth Estimation." Remote Sensing 12, no. 23 (2020): 3905. http://dx.doi.org/10.3390/rs12233905.

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Snow depth and snow water equivalent (SWE) are two parameters for measuring snowfall. By exploiting the Global Navigation Satellite System reflectometry (GNSS-R) technique and thousands of existing GNSS Continuous Operating Reference Stations (CORS) deployed in the cryosphere, it is possible to improve the temporal and spatial resolutions of the SWE measurement. In this paper, a fusion model for combining multi-satellite SNR (Signal to Noise Ratio) snow depth estimations is proposed, which uses peak spectral powers associated with each of the snow depth estimations. To simplify the estimation

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Dai, Yaqiao, Renjiao Yi, Chenyang Zhu, Hongjun He, and Kai Xu. "Multi-Resolution Monocular Depth Map Fusion by Self-Supervised Gradient-Based Composition." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 488–96. http://dx.doi.org/10.1609/aaai.v37i1.25123.

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Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually lack fine-grained details due to convolution operations and down-samplings in networks. We find that increasing input resolution is helpful to preserve more local details while the estimation at low resolution is more accurate globally. Therefore, we propose a novel depth map fusion module to combine the advantages of estimations with multi-resolution inputs. Instead of merging the low- and high-resolution estimations

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Hartmann, Yale, Rinu Elizabeth Paul, Jonah Klöckner, Lucas Deichsel, and Tanja Schultz. "Gait parameter estimation from a single depth sensor." Journal of Smart Cities and Society 4, no. 1 (2025): 35–61. https://doi.org/10.1177/27723577251320237.

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In this article, we present our collected dataset with hardware-synchronized motion capture and depth sensors of a freely moving subject, our estimation of human pose, stride length, step length, and step length classification using deep learning, classical machine learning, and established algorithms. Our results on the 157,825-frame dataset show that pose estimation can be done with up to 85.91% of correct keypoints and as low as 8.86 cm mean per key point error with 2–3 cm of that error attributed to camera imprecision due to the 2–4 m distance. The stride estimation achieves up to 99.58% s

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Cui, Jiadi, Lei Jin, Haofei Kuang, Qingwen Xu, and Sören Schwertfeger. "Underwater Depth Estimation for Spherical Images." Journal of Robotics 2021 (June 17, 2021): 1–12. http://dx.doi.org/10.1155/2021/6644986.

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This paper proposes a method for monocular underwater depth estimation, which is an open problem in robotics and computer vision. To this end, we leverage publicly available in-air RGB-D image pairs for underwater depth estimation in the spherical domain with an unsupervised approach. For this, the in-air images are style-transferred to the underwater style as the first step. Given those synthetic underwater images and their ground truth depth, we then train a network to estimate the depth. This way, our learning model is designed to obtain the depth up to scale, without the need of correspond

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Xiao, Min, Chen Lv, and Xiaomin Liu. "FPattNet: A Multi-Scale Feature Fusion Network with Occlusion Awareness for Depth Estimation of Light Field Images." Sensors 23, no. 17 (2023): 7480. http://dx.doi.org/10.3390/s23177480.

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A light field camera can capture light information from various directions within a scene, allowing for the reconstruction of the scene. The light field image inherently contains the depth information of the scene, and depth estimations of light field images have become a popular research topic. This paper proposes a depth estimation network of light field images with occlusion awareness. Since light field images contain many views from different viewpoints, identifying the combinations that contribute the most to the depth estimation of the center view is critical to improving the depth estim

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30

Patil, Vaishakh, Alexander Liniger, Dengxin Dai, and Luc Van Gool. "Improving Depth Estimation Using Map-Based Depth Priors." IEEE Robotics and Automation Letters 7, no. 2 (2022): 3640–47. http://dx.doi.org/10.1109/lra.2022.3146914.

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31

Cao, Yuanzhouhan, Tianqi Zhao, Ke Xian, Chunhua Shen, Zhiguo Cao, and Shugong Xu. "Monocular Depth Estimation With Augmented Ordinal Depth Relationships." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 8 (2020): 2674–82. http://dx.doi.org/10.1109/tcsvt.2019.2929202.

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Priorelli, Matteo, Giovanni Pezzulo, and Ivilin Peev Stoianov. "Active Vision in Binocular Depth Estimation: A Top-Down Perspective." Biomimetics 8, no. 5 (2023): 445. http://dx.doi.org/10.3390/biomimetics8050445.

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Depth estimation is an ill-posed problem; objects of different shapes or dimensions, even if at different distances, may project to the same image on the retina. Our brain uses several cues for depth estimation, including monocular cues such as motion parallax and binocular cues such as diplopia. However, it remains unclear how the computations required for depth estimation are implemented in biologically plausible ways. State-of-the-art approaches to depth estimation based on deep neural networks implicitly describe the brain as a hierarchical feature detector. Instead, in this paper we propo

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Ibrahem, Hatem, Ahmed Salem, and Hyun-Soo Kang. "DTS-Depth: Real-Time Single-Image Depth Estimation Using Depth-to-Space Image Construction." Sensors 22, no. 5 (2022): 1914. http://dx.doi.org/10.3390/s22051914.

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As most of the recent high-resolution depth-estimation algorithms are computationally so expensive that they cannot work in real time, the common solution is using a low-resolution input image to reduce the computational complexity. We propose a different approach, an efficient and real-time convolutional neural network-based depth-estimation algorithm using a single high-resolution image as the input. The proposed method efficiently constructs a high-resolution depth map using a small encoding architecture and eliminates the need for a decoder, which is typically used in the encoder–decoder a

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Tan, Jiahai, Ming Gao, Tao Duan, and Xiaomei Gao. "A Deep Joint Network for Monocular Depth Estimation Based on Pseudo-Depth Supervision." Mathematics 11, no. 22 (2023): 4645. http://dx.doi.org/10.3390/math11224645.

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Depth estimation from a single image is a significant task. Although deep learning methods hold great promise in this area, they still face a number of challenges, including the limited modeling of nonlocal dependencies, lack of effective loss function joint optimization models, and difficulty in accurately estimating object edges. In order to further increase the network’s prediction accuracy, a new structure and training method are proposed for single-image depth estimation in this research. A pseudo-depth network is first deployed for generating a single-image depth prior, and by constructi

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Yu, Shangbin, Renyan Zhang, Shuaiye Ma, and Xinfang Jiang. "Monocular Depth Estimation Network Based on Swin Transformer." Journal of Physics: Conference Series 2428, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1742-6596/2428/1/012019.

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Abstract Estimating depth from a single image is challenging because a single 2D image may correspond to many different 3D scenes with the same depth. While most deep learning based depth prediction methods extract depth features using small convolutional kernels with small receptive fields, which results in deformed depth edges and inaccurate depth values of distant objects in the depth estimation results. Aiming at this problem, we propose a depth estimation network based on Swin Transformer and the encoder-decoder structure. We construct the encoder using the Swin Transformer network, which

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Lyu, Xiaoyang, Liang Liu, Mengmeng Wang, et al. "HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (2021): 2294–301. http://dx.doi.org/10.1609/aaai.v35i3.16329.

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Self-supervised learning shows great potential in monocular depth estimation, using image sequences as the only source of supervision. Although people try to use the high-resolution image for depth estimation, the accuracy of prediction has not been significantly improved. In this work, we find the core reason comes from the inaccurate depth estimation in large gradient regions, making the bilinear interpolation error gradually disappear as the resolution increases. To obtain more accurate depth estimation in large gradient regions, it is necessary to obtain high-resolution features with spati

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Wu, Shouying, Wei Li, Binbin Liang, and Guoxin Huang. "The Constraints between Edge Depth and Uncertainty for Monocular Depth Estimation." Electronics 10, no. 24 (2021): 3153. http://dx.doi.org/10.3390/electronics10243153.

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The self-supervised monocular depth estimation paradigm has become an important branch of computer vision depth-estimation tasks. However, the depth estimation problem arising from object edge depth pulling or occlusion is still unsolved. The grayscale discontinuity of object edges leads to a relatively high depth uncertainty of pixels in these regions. We improve the geometric edge prediction results by taking uncertainty into account in the depth-estimation task. To this end, we explore how uncertainty affects this task and propose a new self-supervised monocular depth estimation technique b

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Hödel, M., T. Koch, L. Hoegner, and U. Stilla. "MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 55–62. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-55-2019.

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<p><strong>Abstract.</strong> Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly hom

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Fan, Xiule, Ali Jahani Amiri, Baris Fidan, and Soo Jeon. "Unsupervised Stereo Matching with Surface Normal Assistance for Indoor Depth Estimation." Sensors 23, no. 24 (2023): 9850. http://dx.doi.org/10.3390/s23249850.

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To obtain more accurate depth information with stereo cameras, various learning-based stereo-matching algorithms have been developed recently. These algorithms, however, are significantly affected by textureless regions in indoor applications. To address this problem, we propose a new deep-neural-network-based data-driven stereo-matching scheme that utilizes the surface normal. The proposed scheme includes a neural network and a two-stage training strategy. The neural network involves a feature-extraction module, a normal-estimation branch, and a disparity-estimation branch. The training proce

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40

Liu, Zihang, and Quande Wang. "Edge-Enhanced Dual-Stream Perception Network for Monocular Depth Estimation." Electronics 13, no. 9 (2024): 1652. http://dx.doi.org/10.3390/electronics13091652.

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Estimating depth from a single RGB image has a wide range of applications, such as in robot navigation and autonomous driving. Currently, Convolutional Neural Networks based on encoder–decoder architecture are the most popular methods to estimate depth maps. However, convolutional operators have limitations in modeling large-scale dependence, often leading to inaccurate depth predictions at object edges. To address these issues, a new edge-enhanced dual-stream monocular depth estimation method is introduced in this paper. ResNet and Swin Transformer are combined to better extract global and lo

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41

Rychkova, S. I., and V. G. Likhvantseva. "Monocular Depth Estimation (Literature Review)." EYE GLAZ 24, no. 1 (2022): 43–54. http://dx.doi.org/10.33791/2222-4408-2022-1-43-54.

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Background. The physiological basis of spatial perception is traditionally attributed to the binocular system, which integrates the signals coming to the brain from each eye into a single image of the three-dimensional outside world. The perception of three-dimensionality, however, is also possible due to the evolutionarily older monocular system of spatial perception. Normally, the binocular mechanism plays the leading role in depth perception, and its violations lead to a shift towards the monocular. In this regard, one of the relevant areas of ophthalmology and neurophysiology is the study

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Swaraja, K., K. Naga Siva Pavan, S. Suryakanth Reddy, et al. "CNN Based Monocular Depth Estimation." E3S Web of Conferences 309 (2021): 01070. http://dx.doi.org/10.1051/e3sconf/202130901070.

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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 accura

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43

Torralba, A., and A. Oliva. "Depth estimation from image structure." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 9 (2002): 1226–38. http://dx.doi.org/10.1109/tpami.2002.1033214.

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44

Ripka, P., M. Janošek, and P. Nováček. "Depth estimation of metal objects." Procedia Engineering 5 (2010): 280–83. http://dx.doi.org/10.1016/j.proeng.2010.09.102.

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Lee, Woong-Bi, and Heung-No Lee. "Depth-estimation-enabled compound eyes." Optics Communications 412 (April 2018): 178–85. http://dx.doi.org/10.1016/j.optcom.2017.12.009.

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Shang, E. C. "Source depth estimation in waveguides." Journal of the Acoustical Society of America 77, no. 4 (1985): 1413–18. http://dx.doi.org/10.1121/1.392034.

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chaitanya, Soma, Koya Sucharitha, and V. Sridhar. "Depth estimation of face images." IOSR Journal of Electronics and Communication Engineering 9, no. 3 (2014): 35–42. http://dx.doi.org/10.9790/2834-09363542.

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Padkan, N., P. Trybala, R. Battisti, F. Remondino, and C. Bergeret. "EVALUATING MONOCULAR DEPTH ESTIMATION METHODS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023 (October 19, 2023): 137–44. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w3-2023-137-2023.

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Abstract. Depth estimation from monocular images has become a prominent focus in photogrammetry and computer vision research. Monocular Depth Estimation (MDE), which involves determining depth from a single RGB image, offers numerous advantages, including applications in simultaneous localization and mapping (SLAM), scene comprehension, 3D modeling, robotics, and autonomous driving. Depth information retrieval becomes especially crucial in situations where other sources like stereo images, optical flow, or point clouds are not available. In contrast to traditional stereo or multi-view methods,

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Al-Guraibawi, Mohammed, and Baher Mohammed. "Regression Depth for Statistical Depth Function." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (January 14, 2024): 539–46. http://dx.doi.org/10.55562/jrucs.v54i1.621.

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The statistical depth function is one of the modern approaches that can be used for developing multivariate robust regression based on robust estimates of the location and dispersion matrix. One merit advantage of the depth concept is that it can be used directly to provide deeper estimation functions for data location and regression parameters in a multidimensional environment. The deeper estimation functions induced by depth are expected to inherit the desired and inherent robustness properties (such as limited maximum bias, impact function, and high breaking point) as do their counterparts

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Usman, Imran. "An Efficient Depth Estimation Technique Using 3- Trait Luminance Profiling." Engineering, Technology & Applied Science Research 9, no. 4 (2019): 4428–32. https://doi.org/10.5281/zenodo.3370614.

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This paper presents an efficient depth estimation technique for depth image-based rendering process in the 3-D television system. It uses three depth cues, namely linear perspective, motion information, and texture characteristics, to estimate the depth of an image. In addition, suitable weights are assigned to different components of the image based on their relative perspective position of either the foreground or the background in the scene. Experimental results on publicly available datasets validate the usefulness of the proposed technique for efficient estimation of depth maps.

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