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Journal articles on the topic 'Monocular depth'

To see the other types of publications on this topic, follow the link: Monocular depth.

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

Seo, Beom-Su, Byungjae Park, and Hoon Choi. "Sensing Range Extension for Short-Baseline Stereo Camera Using Monocular Depth Estimation." Sensors 22, no. 12 (June 18, 2022): 4605. http://dx.doi.org/10.3390/s22124605.

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This paper proposes a method to extend a sensing range of a short-baseline stereo camera (SBSC). The proposed method combines a stereo depth and a monocular depth estimated by a convolutional neural network-based monocular depth estimation (MDE). To combine a stereo depth and a monocular depth, the proposed method estimates a scale factor of a monocular depth using stereo depth–mono depth pairs and then combines the two depths. Another advantage of the proposed method is that the trained MDE model may be utilized for different environments without retraining. The performance of the proposed method is verified qualitatively and quantitatively using the directly collected and open datasets.

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2

Rychkova, S. I., and V. G. Likhvantseva. "Monocular Depth Estimation (Literature Review)." EYE GLAZ 24, no. 1 (April 2, 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 of the features of monocular depth estimation in normal conditions and cases of ophthalmic pathology.Purpose: to study the literature data on the monocular depth estimation mechanism, methods of its assessment, as well as the peculiarities of its manifestations in normal conditions and cases of ophthalmic pathology.Materials and methods. The literature analysis of publications on PubMed, eLibrary, Cyberleninka and crossref metadata search was carried out.Results. The review considers modern ideas regarding monocular depth cues that can ensure the effective operation of the monocular mechanism of spatial vision. The stereokinetic effect (SE) is considered in detail. The possibilities of using SE assessment methods to evaluate the state of spatial vision mechanisms in cases of ophthalmic and neurological pathology have been studied.Conclusion. There are a number of monocular depth cues that can ensure the effective operation of the monocular mechanism of spatial vision, such as: perspective, light and color effects, accommodation and knowledge of the true sizes of the objects acquired with experience. Stereokinetic effect caused by the successive displacement of projections of circular eccentric images on the retina, which allows to evaluate relationship of monocular and binocular mechanisms of spatial perception, has a particular importance for ophthalmology practice. In patients with binocular vision disorders (amblyopia and strabismus), a decrease in monocular and an increase in binocular SE indicators were observed, whereas only a decrease in monocular indicators is more typical for organic ocular fundus pathology. At the same time, changes in SE indicators can serve as additional criteria for evaluating the efficacy of functional treatment of binocular disorders.

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Pan, Janice, and Alan C. Bovik. "Perceptual Monocular Depth Estimation." Neural Processing Letters 53, no. 2 (February 10, 2021): 1205–28. http://dx.doi.org/10.1007/s11063-021-10437-6.

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4

Howard, I. P., and P. Duke. "Depth from monocular transparency." Journal of Vision 2, no. 10 (December 1, 2002): 82. http://dx.doi.org/10.1167/2.10.82.

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Howard, I. P., and P. Duke. "Depth from monocular images." Journal of Vision 3, no. 9 (March 16, 2010): 463. http://dx.doi.org/10.1167/3.9.463.

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6

Timney, Brian. "Effects of brief monocular deprivation on binocular depth perception in the cat: A sensitive period for the loss of stereopsis." Visual Neuroscience 5, no. 3 (September 1990): 273–80. http://dx.doi.org/10.1017/s0952523800000341.

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AbstractThe period of susceptibility for binocular depth vision was studied in kittens by subjecting them to periods of monocular deprivation beginning at different ages. In an initial study, we found that normally reared kittens can learn a depth-discrimination task much more rapidly when tested binocularly than monocularly, even when testing is begun as early at 30 d. In subsequent experiments, kittens were monocularly deprived by eyelid suture, following which their monocular and binocular depth thresholds were measured using the jumping-stand procedure. We obtained the following results: (1) When monocular deprivation is applied before the time of natural eye opening but is discontinued by no later than 30 d, there is very Little effect on binocular depth thresholds. (2) When deprivation is begun at 90 d, binocular depth thresholds are unaffected. (3) When deprivation is begun between these two ages, the magnitude of the deficit varies with the period of deprivation and the age at which it begins. (4) By imposing brief (5 or 10 d) periods of deprivation, beginning at different ages, we were able to demonstrate that the peak of the sensitive period is between the ages of 35 and 45 d, with a fairly rapid decline in susceptibility outside those age limits. (5) Even with as little as 5 d of deprivation, substantial permanent deficits in binocular depth vision can be induced.

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Swaraja, K., V. Akshitha, K. Pranav, B. Vyshnavi, V. Sai Akhil, K. Meenakshi, Padmavathi Kora, Himabindu Valiveti, and Chaitanya Duggineni. "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 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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8

Munguia, Rodrigo, and Antoni Grau. "Delayed Inverse Depth Monocular SLAM." IFAC Proceedings Volumes 41, no. 2 (2008): 2365–70. http://dx.doi.org/10.3182/20080706-5-kr-1001.00399.

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9

Swaraja, K., K. Naga Siva Pavan, S. Suryakanth Reddy, K. Ajay, P. Uday Kiran Reddy, Padmavathi Kora, K. Meenakshi, Duggineni Chaitanya, and Himabindu Valiveti. "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 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.

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10

Howard, Ian P., and Philip A. Duke. "Monocular transparency generates quantitative depth." Vision Research 43, no. 25 (November 2003): 2615–21. http://dx.doi.org/10.1016/s0042-6989(03)00477-2.

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11

Stevens, Kent A., and Allen Brookes. "Probing depth in monocular images." Biological Cybernetics 56, no. 5-6 (July 1987): 355–66. http://dx.doi.org/10.1007/bf00319515.

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12

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 (August 2020): 2674–82. http://dx.doi.org/10.1109/tcsvt.2019.2929202.

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13

Gillam, Barbara, Michael Cook, and Shane Blackburn. "Monocular Discs in the Occlusion Zones of Binocular Surfaces Do Not Have Quantitative Depth—A Comparison with Panum's Limiting Case." Perception 32, no. 8 (August 2003): 1009–19. http://dx.doi.org/10.1068/p3456.

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Da Vinci stereopsis is defined as apparent depth seen in a monocular object laterally adjacent to a binocular surface in a position consistent with its occlusion by the other eye. It is widely regarded as a new form of quantitative stereopsis because the depth seen is quantitatively related to the lateral separation of the monocular element and the binocular surface (Nakayama and Shimojo 1990 Vision Research30 1811–1825). This can be predicted on the basis that the more separated the monocular element is from the surface the greater its minimum depth behind the surface would have to be to account for its monocular occlusion. Supporting evidence, however, has used narrow bars as the monocular elements, raising the possibility that quantitative depth as a function of separation could be attributable to Panum's limiting case (double fusion) rather than to a new form of stereopsis. We compared the depth performance of monocular objects fusible with the edge of the surface in the contralateral eye (lines) and non-fusible objects (disks) and found that, although the fusible objects showed highly quantitative depth, the disks did not, appearing behind the surface to the same degree at all separations from it. These findings indicate that, although there is a crude sense of depth for discrete monocular objects placed in a valid position for uniocular occlusion, depth is not quantitative. They also indicate that Panum's limiting case is not, as has sometimes been claimed, itself a case of da Vinci stereopsis since fusibility is a critical factor for seeing quantitative depth in discrete monocular objects relative to a binocular surface.

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14

Mendez, Mario F., Monique M. Cherrier, and Robert S. Meadows. "Depth Perception in Alzheimer's Disease." Perceptual and Motor Skills 83, no. 3 (December 1996): 987–95. http://dx.doi.org/10.2466/pms.1996.83.3.987.

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Abnormal depth perception contributes to visuospatial deficits in Alzheimer's disease. Disturbances in stereopsis, motion parallax, and the interpretation of static monocular depth cues may result from neuropathology in the visual cortex. We evaluated 15 patients with mild Alzheimer's disease and 15 controls matched for age, sex, and education on measures of local stereopsis (stereoscopic testing), global stereopsis (random dots), motion parallax (Howard-Dolman apparatus), and monocular depth perception by relative size, interposition, and perspective. Compared to controls, the patients were significantly impaired in over-all depth perception. This impairment was largely due to disturbances in local stereopsis and in the interpretation of depth from perspective, independent of other visuospatial functions. Patients with Alzheimer's disease have disturbed interpretation of monocular as well as binocular depth cues. This information could lead to optic interventions to improve their visual depth perception.

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Khan, Faisal, Saqib Salahuddin, and Hossein Javidnia. "Deep Learning-Based Monocular Depth Estimation Methods—A State-of-the-Art Review." Sensors 20, no. 8 (April 16, 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 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.

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Fan, Chao, Zhenyu Yin, Fulong Xu, Anying Chai, and Feiqing Zhang. "Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation." Sensors 21, no. 21 (October 20, 2021): 6956. http://dx.doi.org/10.3390/s21216956.

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In recent years, self-supervised monocular depth estimation has gained popularity among researchers because it uses only a single camera at a much lower cost than the direct use of laser sensors to acquire depth. Although monocular self-supervised methods can obtain dense depths, the estimation accuracy needs to be further improved for better applications in scenarios such as autonomous driving and robot perception. In this paper, we innovatively combine soft attention and hard attention with two new ideas to improve self-supervised monocular depth estimation: (1) a soft attention module and (2) a hard attention strategy. We integrate the soft attention module in the model architecture to enhance feature extraction in both spatial and channel dimensions, adding only a small number of parameters. Unlike traditional fusion approaches, we use the hard attention strategy to enhance the fusion of generated multi-scale depth predictions. Further experiments demonstrate that our method can achieve the best self-supervised performance both on the standard KITTI benchmark and the Make3D dataset.

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Wang, Rui, Jialing Zou, and James Zhiqing Wen. "SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches." Sensors 21, no. 16 (August 13, 2021): 5476. http://dx.doi.org/10.3390/s21165476.

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Monocular depth estimation based on unsupervised learning has attracted great attention due to the rising demand for lightweight monocular vision sensors. Inspired by multi-task learning, semantic information has been used to improve the monocular depth estimation models. However, multi-task learning is still limited by multi-type annotations. As far as we know, there are scarcely any large public datasets that provide all the necessary information. Therefore, we propose a novel network architecture Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN) to extract multi-resolution depth features and semantic features, which are merged and fed into the decoder, with the goal of predicting depth with the support of semantics. Instead of using loss functions to relate the semantics and depth, the fusion of feature maps for semantics and depth is employed to predict the monocular depth. Therefore, two accessible datasets with similar topics for depth estimation and semantic segmentation can meet the requirements of SFA-MDEN for training sets. We explored the performance of the proposed SFA-MDEN with experiments on different datasets, including KITTI, Make3D, and our own dataset BHDE-v1. The experimental results demonstrate that SFA-MDEN achieves competitive accuracy and generalization capacity compared to state-of-the-art methods.

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18

Zhuo Wang, Zhuo Wang, Min Huang Zhuo Wang, Xiao-Long Huang Min Huang, Fei Man Xiao-Long Huang, Jia-Ming Dou Fei Man, and Jian-li Lyu Jia-Ming Dou. "Unsupervised Learning of Depth and Ego-Motion from Continuous Monocular Images." 電腦學刊 32, no. 6 (December 2021): 038–51. http://dx.doi.org/10.53106/199115992021123206004.

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19

Hristova, H., M. Abegg, C. Fischer, and N. Rehush. "MONOCULAR DEPTH ESTIMATION IN FOREST ENVIRONMENTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 1017–23. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-1017-2022.

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Abstract. Depth estimation from a single image is a challenging task, especially inside the highly structured forest environment. In this paper, we propose a supervised deep learning model for monocular depth estimation based on forest imagery. We train our model on a new data set of forest RGB-D images that we collected using a terrestrial laser scanner. Alongside the input RGB image, our model uses a sparse depth channel as input to recover the dense depth information. The prediction accuracy of our model is significantly higher than that of state-of-the-art methods when applied in the context of forest depth estimation. Our model brings the RMSE down to 2.1 m, compared to 4 m and above for reference methods.

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Welponer, M., E. K. Stathopoulou, and F. Remondino. "MONOCULAR DEPTH PREDICTION IN PHOTOGRAMMETRIC APPLICATIONS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 469–76. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-469-2022.

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Abstract. Despite the recent success of learning-based monocular depth estimation algorithms and the release of large-scale datasets for training, the methods are limited to depth map prediction and still struggle to yield reliable results in the 3D space without additional scene cues. Indeed, although state-of-the-art approaches produce quality depth maps, they generally fail to recover the 3D structure of the scene robustly. This work explores supervised CNN architectures for monocular depth estimation and evaluates their potential in 3D reconstruction. Since most available datasets for training are not designed toward this goal and are limited to specific indoor scenarios, a new metric, large-scale synthetic benchmark (ArchDepth) is introduced that renders near real-world scenarios of outdoor scenes. A encoder-decoder architecture is used for training, and the generalization of the approach is evaluated via depth inference in unseen views in synthetic and real-world scenarios. The depth map predictions are also projected in the 3D space using a separate module. Results are qualitatively and quantitatively evaluated and compared with state-of-the-art algorithms for single image 3D scene recovery.

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21

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 homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.</p>

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22

Ji, Rongrong, Ke Li, Yan Wang, Xiaoshuai Sun, Feng Guo, Xiaowei Guo, Yongjian Wu, Feiyue Huang, and Jiebo Luo. "Semi-Supervised Adversarial Monocular Depth Estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 10 (October 1, 2020): 2410–22. http://dx.doi.org/10.1109/tpami.2019.2936024.

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23

Civera, J., A. J. Davison, and J. Montiel. "Inverse Depth Parametrization for Monocular SLAM." IEEE Transactions on Robotics 24, no. 5 (October 2008): 932–45. http://dx.doi.org/10.1109/tro.2008.2003276.

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KAYE, STEPHEN B., AKMAL SIDDIQUI, ANGELA WARD, CARMEL NOONAN, ANTHONY C. FISHER, MALCOLM C. BROWN, PAUL A. WAREING, PETER WATT, and JOHN R. GREEN. "Monocular and Binocular Depth Discrimination Thresholds." Optometry and Vision Science 76, no. 11 (November 1999): 770–82. http://dx.doi.org/10.1097/00006324-199911000-00026.

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25

Shimono, Koichi, and Nicholas J. Wade. "Monocular alignment in different depth planes." Vision Research 42, no. 9 (April 2002): 1127–35. http://dx.doi.org/10.1016/s0042-6989(02)00051-2.

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26

Hsu, Li-Chuan, Peter Kramer, and Su-Ling Yeh. "Monocular depth effects on perceptual fading." Vision Research 50, no. 17 (August 2010): 1649–55. http://dx.doi.org/10.1016/j.visres.2010.05.008.

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27

Mathew, Alwyn, and Jimson Mathew. "Monocular depth estimation with SPN loss." Image and Vision Computing 100 (August 2020): 103934. http://dx.doi.org/10.1016/j.imavis.2020.103934.

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28

Li, Chuxuan, Ran Yi, Saba Ghazanfar Ali, Lizhuang Ma, Enhua Wu, Jihong Wang, Lijuan Mao, and Bin Sheng. "RADepthNet: Reflectance-Aware Monocular Depth Estimation." Virtual Reality & Intelligent Hardware 4, no. 5 (October 2022): 418–31. http://dx.doi.org/10.1016/j.vrih.2022.08.005.

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Yang, Xin, Qingling Chang, Shiting Xu, Xinlin Liu, and Yan Cui. "Monocular Depth Estimation with Sharp Boundary." Computer Modeling in Engineering & Sciences 136, no. 1 (2023): 573–92. http://dx.doi.org/10.32604/cmes.2023.023424.

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Yang, Yi, Lihua Tian, Chen Li, and Botong Zhang. "Multi-scale depth classification network for monocular depth estimation." Computers and Electrical Engineering 102 (September 2022): 108206. http://dx.doi.org/10.1016/j.compeleceng.2022.108206.

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Lyu, Xiaoyang, Liang Liu, Mengmeng Wang, Xin Kong, Lina Liu, Yong Liu, Xinxin Chen, and Yi Yuan. "HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 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 spatial and semantic information. Therefore, we present an improved DepthNet, HR-Depth, with two effective strategies: (1) re-design the skip-connection in DepthNet to get better high-resolution features and (2) propose feature fusion Squeeze-and-Excitation(fSE) module to fuse feature more efficiently. Using Resnet-18 as the encoder, HR-Depth surpasses all previous state-of-the-art(SoTA) methods with the least parameters at both high and low resolution. Moreover, previous SoTA methods are based on fairly complex and deep networks with a mass of parameters which limits their real applications. Thus we also construct a lightweight network which uses MobileNetV3 as encoder. Experiments show that the lightweight network can perform on par with many large models like Monodepth2 at high-resolution with only20%parameters. All codes and models will be available at https://github.com/shawLyu/HR-Depth.

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Zhang, Zhimin, Jianzhong Qiao, and Shukuan Lin. "A Semi-Supervised Monocular Stereo Matching Method." Symmetry 11, no. 5 (May 18, 2019): 690. http://dx.doi.org/10.3390/sym11050690.

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Supervised monocular depth estimation methods based on learning have shown promising results compared with the traditional methods. However, these methods require a large number of high-quality corresponding ground truth depth data as supervision labels. Due to the limitation of acquisition equipment, it is expensive and impractical to record ground truth depth for different scenes. Compared to supervised methods, the self-supervised monocular depth estimation method without using ground truth depth is a promising research direction, but self-supervised depth estimation from a single image is geometrically ambiguous and suboptimal. In this paper, we propose a novel semi-supervised monocular stereo matching method based on existing approaches to improve the accuracy of depth estimation. This idea is inspired by the experimental results of the paper that the depth estimation accuracy of a stereo pair as input is better than that of a monocular view as input in the same self-supervised network model. Therefore, we decompose the monocular depth estimation problem into two sub-problems, a right view synthesized process followed by a semi-supervised stereo matching process. In order to improve the accuracy of the synthetic right view, we innovate beyond the existing view synthesis method Deep3D by adding a left-right consistency constraint and a smoothness constraint. To reduce the error caused by the reconstructed right view, we propose a semi-supervised stereo matching model that makes use of disparity maps generated by a self-supervised stereo matching model as the supervision cues and joint self-supervised cues to optimize the stereo matching network. In the test, the two networks are able to predict the depth map directly from a single image by pipeline connecting. Both procedures not only obey geometric principles, but also improve estimation accuracy. Test results on the KITTI dataset show that this method is superior to the current mainstream monocular self-supervised depth estimation methods under the same condition.

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Sen, Maya G., Albert Yonas, and David C. Knill. "Development of Infants' Sensitivity to Surface Contour Information for Spatial Layout." Perception 30, no. 2 (February 2001): 167–76. http://dx.doi.org/10.1068/p2789.

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The development of sensitivity to a recently discovered static-monocular depth cue to surface shape, surface contours, was investigated. Twenty infants in each of three age groups (5, 5½, and 7 months) viewed a display that creates an illusion, for adult viewers, that what is in fact a frontoparallel cylinder is slanted away in depth, so that one end appears closer than the other. Preferential reaching was recorded in both monocular and binocular conditions. More reaching to the apparently closer end in the monocular than in the binocular condition is evidence of sensitivity. Infants aged 7 months responded to surface contour information, but infants aged 5 and 5 months did not. In a control study, twenty 5-month-old infants reached consistently for the closer ends of cylinders that were actually rotated in depth. As findings with other static-monocular depth information suggest, infants' sensitivity to surface contour information appears to develop at approximately 6 months.

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Hartle, Brittney, Aishwarya Sudhama, Lesley M. Deas, Robert S. Allison, Elizabeth L. Irving, Mackenzie G. Glaholt, and Laurie M. Wilcox. "Contributions of Stereopsis and Aviation Experience to Simulated Rotary Wing Altitude Estimation." Human Factors: The Journal of the Human Factors and Ergonomics Society 62, no. 5 (June 18, 2019): 812–24. http://dx.doi.org/10.1177/0018720819853479.

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Objective We examined the contribution of binocular vision and experience to performance on a simulated helicopter flight task. Background Although there is a long history of research on the role of binocular vision and stereopsis in aviation, there is no consensus on its operational relevance. This work addresses this using a naturalistic task in a virtual environment. Method Four high-resolution stereoscopic terrain types were viewed monocularly and binocularly. In separate experiments, we evaluated performance of undergraduate students and military aircrew on a simulated low hover altitude judgment task. Observers were asked to judge the distance between a virtual helicopter skid and the ground plane. Results Our results show that for both groups, altitude judgments are more accurate in the binocular viewing condition than in the monocular condition. However, in the monocular condition, aircrew were more accurate than undergraduate observers in estimating height of the skid above the ground. Conclusion At simulated altitudes of 5 ft (1.5 m) or less, binocular vision provides a significant advantage for estimation of the depth separation between the landing skid and the ground, regardless of relevant operational experience. However, when binocular cues are unavailable aircrew outperform undergraduate observers, a result that likely reflects the impact of training on the ability to interpret monocular depth cues.

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Hao, Yang, Jing Li, Fei Meng, Peisen Zhang, Gastone Ciuti, Paolo Dario, and Qiang Huang. "Photometric Stereo-Based Depth Map Reconstruction for Monocular Capsule Endoscopy." Sensors 20, no. 18 (September 21, 2020): 5403. http://dx.doi.org/10.3390/s20185403.

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The capsule endoscopy robot can only use monocular vision due to the dimensional limit. To improve the depth perception of the monocular capsule endoscopy robot, this paper proposes a photometric stereo-based depth map reconstruction method. First, based on the characteristics of the capsule endoscopy robot system, a photometric stereo framework is established. Then, by combining the specular property and Lambertian property of the object surface, the depth of the specular highlight point is estimated, and the depth map of the whole object surface is reconstructed by a forward upwind scheme. To evaluate the precision of the depth estimation of the specular highlight region and the depth map reconstruction of the object surface, simulations and experiments are implemented with synthetic images and pig colon tissue, respectively. The results of the simulations and experiments show that the proposed method provides good precision for depth map reconstruction in monocular capsule endoscopy.

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Song, Chuanxue, Chunyang Qi, Shixin Song, and Feng Xiao. "Unsupervised Monocular Depth Estimation Method Based on Uncertainty Analysis and Retinex Algorithm." Sensors 20, no. 18 (September 21, 2020): 5389. http://dx.doi.org/10.3390/s20185389.

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Depth estimation of a single image presents a classic problem for computer vision, and is important for the 3D reconstruction of scenes, augmented reality, and object detection. At present, most researchers are beginning to focus on unsupervised monocular depth estimation. This paper proposes solutions to the current depth estimation problem. These solutions include a monocular depth estimation method based on uncertainty analysis, which solves the problem in which a neural network has strong expressive ability but cannot evaluate the reliability of an output result. In addition, this paper proposes a photometric loss function based on the Retinex algorithm, which solves the problem of pulling around pixels due to the presence of moving objects. We objectively compare our method to current mainstream monocular depth estimation methods and obtain satisfactory results.

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Yang, Xin, Qingling Chang, Xinglin Liu, Siyuan He, and Yan Cui. "Monocular Depth Estimation Based on Multi-Scale Depth Map Fusion." IEEE Access 9 (2021): 67696–705. http://dx.doi.org/10.1109/access.2021.3076346.

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Papathomas, Thomas V., Akos Feher, Bela Julesz, and Yehoshua Zeevi. "Interactions of Monocular and Cyclopean Components and the Role of Depth in the Ebbinghaus Illusion." Perception 25, no. 7 (July 1996): 783–95. http://dx.doi.org/10.1068/p250783.

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A study of size interactions of objects in three-dimensional space is reported. The canonical form of the Ebbinghaus illusion—test circles surrounded by large or small inducers—was used. Both monocularly visible (M) and purely cyclopean (C) objects were displayed stereoscopically to isolate the monocular and cyclopean components of the illusion. The results of two experiments indicate that: (i) depth plays a significant role when the test circles are cyclopean, but not when they are monocularly visible; and (ii) the size of C objects is affected equally by C and M inducers, but the size of M objects is affected much more strongly by M than by C inducers. In conclusion, possible explanations are offered for the main trends in the data, the most interesting of which is that cyclopean tests seem to be interacting only with the cyclopean component of monocularly visible inducers.

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Wang, Xinlong, Wei Yin, Tao Kong, Yuning Jiang, Lei Li, and Chunhua Shen. "Task-Aware Monocular Depth Estimation for 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12257–64. http://dx.doi.org/10.1609/aaai.v34i07.6908.

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Monocular depth estimation enables 3D perception from a single 2D image, thus attracting much research attention for years. Almost all methods treat foreground and background regions (“things and stuff”) in an image equally. However, not all pixels are equal. Depth of foreground objects plays a crucial role in 3D object recognition and localization. To date how to boost the depth prediction accuracy of foreground objects is rarely discussed. In this paper, we first analyze the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground and background depth using separate optimization objectives and decoders. Our method significantly improves the depth estimation performance on foreground objects. Applying ForeSeE to 3D object detection, we achieve 7.5 AP gains and set new state-of-the-art results among other monocular methods. Code will be available at: https://github.com/WXinlong/ForeSeE.

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Huang, Tao, Shuanfeng Zhao, Longlong Geng, and Qian Xu. "Unsupervised Monocular Depth Estimation Based on Residual Neural Network of Coarse–Refined Feature Extractions for Drone." Electronics 8, no. 10 (October 17, 2019): 1179. http://dx.doi.org/10.3390/electronics8101179.

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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.

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Cheda, Diego. "Monocular Depth Cues in Computer Vision Applications." ELCVIA Electronic Letters on Computer Vision and Image Analysis 13, no. 2 (June 7, 2014): 65. http://dx.doi.org/10.5565/rev/elcvia.620.

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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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Li, Shuai, Jiaying Shi, Wenfeng Song, Aimin Hao, and Hong Qin. "Hierarchical Object Relationship Constrained Monocular Depth Estimation." Pattern Recognition 120 (December 2021): 108116. http://dx.doi.org/10.1016/j.patcog.2021.108116.

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Lu, Xiao, Haoran Sun, Xiuling Wang, Zhiguo Zhang, and Haixia Wang. "Semantically guided self‐supervised monocular depth estimation." IET Image Processing 16, no. 5 (January 7, 2022): 1293–304. http://dx.doi.org/10.1049/ipr2.12409.

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Wu, Kewei, Shunran Zhang, and Zhao Xie. "Monocular Depth Prediction With Residual DenseASPP Network." IEEE Access 8 (2020): 129899–910. http://dx.doi.org/10.1109/access.2020.3006704.

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Kim, Doyeon, Sihaeng Lee, Janghyeon Lee, and Junmo Kim. "Leveraging Contextual Information for Monocular Depth Estimation." IEEE Access 8 (2020): 147808–17. http://dx.doi.org/10.1109/access.2020.3016008.

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Su, Che-Chun, Lawrence K. Cormack, and Alan C. Bovik. "Bayesian depth estimation from monocular natural images." Journal of Vision 17, no. 5 (May 26, 2017): 22. http://dx.doi.org/10.1167/17.5.22.

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Martin, Martin C. "Evolving visual sonar: Depth from monocular images." Pattern Recognition Letters 27, no. 11 (August 2006): 1174–80. http://dx.doi.org/10.1016/j.patrec.2005.07.015.

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Brooks, K. R., and B. J. Gillam. "Quantitative perceived depth from sequential monocular decamouflage." Vision Research 46, no. 5 (March 2006): 605–13. http://dx.doi.org/10.1016/j.visres.2005.06.015.

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Tsirlin, Inna, Laurie M. Wilcox, and Robert S. Allison. "Disparity biasing in depth from monocular occlusions." Vision Research 51, no. 14 (July 2011): 1699–711. http://dx.doi.org/10.1016/j.visres.2011.05.012.

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