Relevant bibliographies by topics / Monocular depth

Academic literature on the topic 'Monocular depth'

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Published: 4 June 2021
Last updated: 26 January 2023

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Journal articles on the topic "Monocular depth"

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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Monocular depth"

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Andraghetti, Lorenzo. "Monocular Depth Estimation enhancement by depth from SLAM Keypoints." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16626/.

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Training a neural network in a supervised way is extremely challenging since ground truth is expensive, time consuming and limited. Therefore the best choice is to do it unsupervisedly, exploiting easier-to-obtain binocular stereo images and epipolar geometry constraints. Sometimes however, this is not enough to predict fairly correct depth maps because of ambiguity of colour images, due for instance to shadows, reflective surfaces and so on. A Simultaneous Location and Mapping (SLAM) algorithm keeps track of hundreds of 3D landmarks in each frame of a sequence. Therefore, given the base assumption that it has the right scale, it can help the depth prediction providing a value for each of those 3D points. This work proposes a novel approach to enhance the depth prediction exploiting the potential of the SLAM depth points to their limits.
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Pinheiro, de Carvalho Marcela. "Deep Depth from Defocus : Neural Networks for Monocular Depth Estimation." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS609.

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L'estimation de profondeur à partir d'une seule image est maintenant cruciale pour plusieurs applications, de la robotique à la réalité virtuelle. Les approches par apprentissage profond dans les tâches de vision par ordinateur telles que la reconnaissance et la classification d'objets ont également apporté des améliorations au domaine de l'estimation de profondeur. Dans cette thèse, nous développons des méthodes pour l'estimation en profondeur avec un réseau de neurones profond en explorant différents indices, tels que le flou de défocalisation et la sémantique. Nous menons également plusieures expériences pour comprendre la contribution de chaque indice à la performance du modèle et sa capacité de généralisation. Dans un premier temps, nous proposons un réseau de neurones convolutif efficace pour l'estimation de la profondeur ainsi qu'une stratégie d'entraînement basée sur les réseaux génératifs adversaires conditionnels. Notre méthode permet d'obtenir des performances parmis les meilleures sur les jeux de données standard. Ensuite, nous proposons d'explorer le flou de défocalisation, une information optique fondamentalement liée à la profondeur. Nous montrons que ces modèles sont capables d'apprendre et d'utiliser implicitement cette information pour améliorer les performances et dépasser les limitations connues des approches classiques d'estimation de la profondeur par flou de défocalisation. Nous construisons également une nouvelle base de données avec de vraies images focalisées et défocalisées que nous utilisons pour valider notre approche. Enfin, nous explorons l'utilisation de l'information sémantique, qui apporte une information contextuelle riche, en apprenant à la prédire conjointement avec la profondeur par une approache multi-tâche
Depth estimation from a single image is a key instrument for several applications from robotics to virtual reality. Successful Deep Learning approaches in computer vision tasks as object recognition and classification also benefited the domain of depth estimation. In this thesis, we develop methods for monocular depth estimation with deep neural network by exploring different cues: defocus blur and semantics. We conduct several experiments to understand the contribution of each cue in terms of generalization and model performance. At first, we propose an efficient convolutional neural network for depth estimation along with a conditional Generative Adversarial framework. Our method achieves performances among the best on standard datasets for depth estimation. Then, we propose to explore defocus blur cues, which is an optical information deeply related to depth. We show that deep models are able to implicitly learn and use this information to improve performance and overcome known limitations of classical Depth-from-Defocus. We also build a new dataset with real focused and defocused images that we use to validate our approach. Finally, we explore the use of semantic information, which brings rich contextual information while learned jointly to depth on a multi-task approach. We validate our approaches with several datasets containing indoor, outdoor and aerial images
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Cheda, Diego. "Monocular Depth Cues in Computer Vision Applications." Doctoral thesis, Universitat Autònoma de Barcelona, 2012. http://hdl.handle.net/10803/121644.

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La percepción de la profundidad es un aspecto clave en la visión humana. El ser humano realiza esta tarea sin esfuerzo alguno con el objetivo de efectuar diversas actividades cotidianas. A menudo, la percepción de la profundidad se ha asociado con la visión binocular. Pese a esto, los seres humanos tienen una capacidad asombrosa de percibir las relaciones de profundidad, incluso a partir de una sola imagen, mediante el uso de varias pistas monoculares. En el campo de la visión por ordenador, si la información de la profundidad de una imagen estuviera disponible, muchas tareas podr´ıan ser planteadas desde una perspectiva diferente en aras de un mayor rendimiento y robustez. Sin embargo, dada una única imagen, esta posibilidad es generalmente descartada, ya que la obtención de la información de profundidad es frecuentemente obtenida por las técnicas de reconstrucción tridimensional, que requieren dos o más imágenes de la misma escena tomadas desde diferentes puntos de vista. Recientemente, algunas propuestas han demostrado que es posible obtener información de profundidad a partir de imágenes individuales. En esencia, la idea es aprovechar el conocimiento a priori de las condiciones de adquisición de la imagen y de la escena observada para estimar la profundidad empleando pistas pictóricas monoculares. Estos enfoques tratan de estimar con precisión los mapas de profundidad de la escena empleando técnicas computacionalmente costosas. Sin embargo, muchos algoritmos de visión por ordenador no necesitan un mapa de profundidad detallado de la imagen. De hecho, sólo una descripción en profundidad aproximada puede ser muy valiosa en muchos problemas. En nuestro trabajo, hemos demostrado que incluso la información aproximada de profundidad puede integrarse en diferentes tareas siguiendo una estrategia holística con el fin de obtener resultados más precisos y robustos. En ese sentido, hemos propuesto una técnica simple, pero fiable, por medio de la cual regiones de la imagen de una escena se clasifican en rangos de profundidad discretos para construir un mapa tosco de la profundidad. Sobre la base de esta representación, hemos explorado la utilidad de nuestro método en tres dominios de aplicación desde puntos de vista novedosos: la estimación de la rotación de la cámara, la estimación del fondo de una escena y la generación de ventanas de interés para la detección de peatones. En el primer caso, calculamos la rotación de la cámara montada en un veh´ıculo en movimiento mediante dos nuevos m˜A c ⃝todos que identifican elementos distantes en la imagen a través de nuestros mapas de profundidad. En la reconstrucción del fondo de una imagen, propusimos un método novedoso que penaliza las regiones cercanas en una función de coste que integra, además, información del color y del movimiento. Por último, empleamos la información geométrica y de la profundidad de una escena para la generación de peatones candidatos. Este método reduce significativamente el número de ventanas generadas, las cuales serán posteriormente procesadas por un clasificador de peatones. En todos los casos, los resultados muestran que los enfoques basados en la profundidad contribuyen a un mejor rendimiento de las aplicaciones estudidadas.
Depth perception is a key aspect of human vision. It is a routine and essential visual task that the human do effortlessly in many daily activities. This has often been associated with stereo vision, but humans have an amazing ability to perceive depth relations even from a single image by using several monocular cues. In the computer vision field, if image depth information were available, many tasks could be posed from a different perspective for the sake of higher performance and robustness. Nevertheless, given a single image, this possibility is usually discarded, since obtaining depth information has frequently been performed by three-dimensional reconstruction techniques, requiring two or more images of the same scene taken from different viewpoints. Recently, some proposals have shown the feasibility of computing depth information from single images. In essence, the idea is to take advantage of a priori knowledge of the acquisition conditions and the observed scene to estimate depth from monocular pictorial cues. These approaches try to precisely estimate the scene depth maps by employing computationally demanding techniques. However, to assist many computer vision algorithms, it is not really necessary computing a costly and detailed depth map of the image. Indeed, just a rough depth description can be very valuable in many problems. In this thesis, we have demonstrated how coarse depth information can be integrated in different tasks following holistic and alternative strategies to obtain more precise and robustness results. In that sense, we have proposed a simple, but reliable enough technique, whereby image scene regions are categorized into discrete depth ranges to build a coarse depth map. Based on this representation, we have explored the potential usefulness of our method in three application domains from novel viewpoints: camera rotation parameters estimation, background estimation and pedestrian candidate generation. In the first case, we have computed camera rotation mounted in a moving vehicle from two novels methods that identify distant elements in the image, where the translation component of the image flow field is negligible. In background estimation, we have proposed a novel method to reconstruct the background by penalizing close regions in a cost function, which integrates color, motion, and depth terms. Finally, we have benefited of geometric and depth information available on single images for pedestrian candidate generation to significantly reduce the number of generated windows to be further processed by a pedestrian classifier. In all cases, results have shown that our depth-based approaches contribute to better performances.
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Toschi, Marco. "Towards Monocular Depth Estimation for Robot Guidance." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Human visual perception is a powerful tool to let us interact with the world, interpreting depth using both physiological and psychological cues. In the early days, machine vision was primarily inspired by physiological cues, guiding robots with bulky sensors based on focal length adjustments, pattern matching, and binocular disparity. In reality, however, we always get a certain degree of depth sensation from the monocular image reproduced on the retina, which is judged by our brain upon empirical grounds. With the advent of deep learning techniques, estimating depth from a monocular image has became a major research topic. Currently, it is still far from industrial use, as the estimated depth is valid only up to a scale factor, leaving us with relative depth information. We propose an algorithm to estimate the depth of a scene at the actual global scale, leveraging geometric constraints and state-of-the-art techniques in optical flow and depth estimation. We first compute the three-dimensional information of multiple similar scenes, triangulating multi-view images for which dense correspondences have been estimated by an Optical Flow Estimation network. Then we train a Monocular Depth Estimation network on the precomputed multiple scenes to learn their similarities, like objects sizes, and ignore their differences, like objects arrangements. Experimental results suggest that our method is able to learn to estimate metric depth of a novel similar scene, opening the possibility to perform Robot Guidance using an affordable, light and compact smartphone camera as depth sensor.
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Rovinelli, Marco. "Realtime Monocular Depth Estimation on Mobile Phones." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24159/.

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Depth estimation is a necessary task to understand and navigate the environment around us. Over the years, many active sensors have been developed to measure depth but they are expensive and require additional space to be mounted. A cheaper alternative consists of estimating depth maps using images taken by a mobile phone camera. Since most mobile phones don't have cameras built for stereo depth sensing, it would be ideal to be able to recover depth from a single image using only the computational capability of the mobile phone itself. This can be achieved by training a neural network on ground truth depth maps. This type of data is very expensive to obtain so it's preferred to train the neural network using self-supervision from multiple images. Since the devices where the trained models will be deployed have only one camera, it is ideal to train the network on monocular videos representing the actual data distribution at deployment. Self-supervised training using monocular videos lowers the accuracy of the depth maps and brings the additional challenge of being able to predict depth only up to an unknown scale factor. To this end, additional information, velocity provided by the GPS, and sparse points computed by a monocular SLAM algorithm, are employed to recover scale and improve the accuracy. This study will investigate different neural network architectures and training schemes to achieve depth maps as accurately as possible given the constraints of the computational budget available on modern mobile phones.
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Rivero, Pindado Víctor. "Monocular visual SLAM based on Inverse depth parametrization." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-10166.

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The first objective of this research has always been carry out a study of visual techniques SLAM (Simultaneous localization and mapping), specifically the type monovisual, less studied than the stereo. These techniques have been well studied in the world of robotics. These techniques are focused on reconstruct a map of the robot enviroment while maintaining its position information in that map. We chose to investigate a method to encode the points by the inverse of its depth, from the first time that the feature was observed. This method permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF).At first, the study mentioned it should be consolidated developing an application that implements this method. After suffering various difficulties, it was decided to make use of a platform developed by the same author of Slam method mentioned in MATLAB. Until then it had developed the tasks of calibration, feature extraction and matching. From that point, that application was adapted to the characteristics of our camera and our video to work. We recorded a video with our camera following a known trajectory to check the calculated path shown in the application. Corroborating works and studying the limitations and advantages of this method.

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Chan, Kevin S. (Kevin Sao Wei). "Multiview monocular depth estimation using unsupervised learning methods." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119753.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 50-51).
Existing learned methods for monocular depth estimation use only a single view of scene for depth evaluation, so they inherently overt to their training scenes and cannot generalize well to new datasets. This thesis presents a neural network for multiview monocular depth estimation. Teaching a network to estimate depth via structure from motion allows it to generalize better to new environments with unfamiliar objects. This thesis extends recent work in unsupervised methods for single-view monocular depth estimation and uses the reconstruction losses for training posed in those works. Models and baseline models were evaluated on a variety of datasets and results indicate that indicate multiview models generalize across datasets better than previous work. This work is unique in that it emphasizes cross domain performance and ability to generalize more so than performance on the training set.
by Kevin S. Chan.
M. Eng.
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Larsson, Susanna. "Monocular Depth Estimation Using Deep Convolutional Neural Networks." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-159981.

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For a long time stereo-cameras have been deployed in visual Simultaneous Localization And Mapping (SLAM) systems to gain 3D information. Even though stereo-cameras show good performance, the main disadvantage is the complex and expensive hardware setup it requires, which limits the use of the system. A simpler and cheaper alternative are monocular cameras, however monocular images lack the important depth information. Recent works have shown that having access to depth maps in monocular SLAM system is beneficial since they can be used to improve the 3D reconstruction. This work proposes a deep neural network that predicts dense high-resolution depth maps from monocular RGB images by casting the problem as a supervised regression task. The network architecture follows an encoder-decoder structure in which multi-scale information is captured and skip-connections are used to recover details. The network is trained and evaluated on the KITTI dataset achieving results comparable to state-of-the-art methods. With further development, this network shows good potential to be incorporated in a monocular SLAM system to improve the 3D reconstruction.
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Möckelind, Christoffer. "Improving deep monocular depth predictions using dense narrow field of view depth images." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235660.

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In this work we study a depth prediction problem where we provide a narrow field of view depth image and a wide field of view RGB image to a deep network tasked with predicting the depth for the entire RGB image. We show that by providing a narrow field of view depth image, we improve results for the area outside the provided depth compared to an earlier approach only utilizing a single RGB image for depth prediction. We also show that larger depth maps provide a greater advantage than smaller ones and that the accuracy of the model decreases with the distance from the provided depth. Further, we investigate several architectures as well as study the effect of adding noise and lowering the resolution of the provided depth image. Our results show that models provided low resolution noisy data performs on par with the models provided unaltered depth.
I det här arbetet studerar vi ett djupapproximationsproblem där vi tillhandahåller en djupbild med smal synvinkel och en RGB-bild med bred synvinkel till ett djupt nätverk med uppgift att förutsäga djupet för hela RGB-bilden. Vi visar att genom att ge djupbilden till nätverket förbättras resultatet för området utanför det tillhandahållna djupet jämfört med en existerande metod som använder en RGB-bild för att förutsäga djupet. Vi undersöker flera arkitekturer och storlekar på djupbildssynfält och studerar effekten av att lägga till brus och sänka upplösningen på djupbilden. Vi visar att större synfält för djupbilden ger en större fördel och även att modellens noggrannhet minskar med avståndet från det angivna djupet. Våra resultat visar också att modellerna som använde sig av det brusiga lågupplösta djupet presterade på samma nivå som de modeller som använde sig av det omodifierade djupet.
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Pilzer, Andrea. "Learning Unsupervised Depth Estimation, from Stereo to Monocular Images." Doctoral thesis, Università degli studi di Trento, 2020. http://hdl.handle.net/11572/268252.

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In order to interact with the real world, humans need to perform several tasks such as object detection, pose estimation, motion estimation and distance estimation. These tasks are all part of scene understanding and are fundamental tasks of computer vision. Depth estimation received unprecedented attention from the research community in recent years due to the growing interest in its practical applications (ie robotics, autonomous driving, etc.) and the performance improvements achieved with deep learning. In fact, the applications expanded from the more traditional tasks such as robotics to new fields such as autonomous driving, augmented reality devices and smartphones applications. This is due to several factors. First, with the increased availability of training data, bigger and bigger datasets were collected. Second, deep learning frameworks running on graphical cards exponentially increased the data processing capabilities allowing for higher precision deep convolutional networks, ConvNets, to be trained. Third, researchers applied unsupervised optimization objectives to ConvNets overcoming the hurdle of collecting expensive ground truth and fully exploiting the abundance of images available in datasets. This thesis addresses several proposals and their benefits for unsupervised depth estimation, i.e., (i) learning from resynthesized data, (ii) adversarial learning, (iii) coupling generator and discriminator losses for collaborative training, and (iv) self-improvement ability of the learned model. For the first two points, we developed a binocular stereo unsupervised depth estimation model that uses reconstructed data as an additional self-constraint during training. In addition to that, adversarial learning improves the quality of the reconstructions, further increasing the performance of the model. The third point is inspired by scene understanding as a structured task. A generator and a discriminator joining their efforts in a structured way improve the quality of the estimations. Our intuition may sound counterintuitive when cast in the general framework of adversarial learning. However, in our experiments we demonstrate the effectiveness of the proposed approach. Finally, self-improvement is inspired by estimation refinement, a widespread practice in dense reconstruction tasks like depth estimation. We devise a monocular unsupervised depth estimation approach, which measures the reconstruction errors in an unsupervised way, to produce a refinement of the depth predictions. Furthermore, we apply knowledge distillation to improve the student ConvNet with the knowledge of the teacher ConvNet that has access to the errors.
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Books on the topic "Monocular depth"

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Arden, P. L. C. Monocular Stereopsis: Seeing in Depth with One Eye. Palgrave Macmillan, 2017.

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Ganeri, Jonardon. Postscript: Philosophy Without Borders. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198757405.003.0017.

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This postscript discusses the nature and ambitions of cross-cultural philosophy. It distinguishes cross-cultural philosophy from an older project of comparative philosophy, and argues that philosophy should be a cosmopolitan undertaking. A cross-cultural philosophy claims that it is methodologically essential to consider theories from a plurality of cultural locations if one’s ambition is to discover a fundamental theory true of the human mind as such. So philosophy should be ‘borderless’, straddling geographical and cultural divisions. To think across cultures and languages is somewhat akin to perceiving with two eyes rather one, in that one gains a depth of vision not available in monocular sight.
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Book chapters on the topic "Monocular depth"

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Jun, Jinyoung, Jae-Han Lee, Chul Lee, and Chang-Su Kim. "Depth Map Decomposition for Monocular Depth Estimation." In Lecture Notes in Computer Science, 18–34. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20086-1_2.

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House, Donald. "Monocular and Binocular Cooperation." In Depth Perception in Frogs and Toads, 31–56. New York, NY: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-6391-0_3.

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Huynh, Lam, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä. "Guiding Monocular Depth Estimation Using Depth-Attention Volume." In Computer Vision – ECCV 2020, 581–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58574-7_35.

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Zhang, Jinqing, Haosong Yue, Xingming Wu, Weihai Chen, and Changyun Wen. "Densely Connecting Depth Maps for Monocular Depth Estimation." In Computer Vision – ECCV 2020 Workshops, 149–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66823-5_9.

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Clark, James J., and Alan L. Yuille. "Fusing Binocular and Monocular Depth Cues." In Data Fusion for Sensory Information Processing Systems, 137–46. Boston, MA: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-2076-1_6.

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Leroy, Jean-Vincent, Thierry Simon, and François Deschenes. "Real Time Monocular Depth from Defocus." In Lecture Notes in Computer Science, 103–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69905-7_12.

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Chaudhari, Shubham, Aaryamaan Rao, Rohit Vardam, and Mandar Sohani. "A Synopsis of Monocular Depth Estimation." In Lecture Notes in Electrical Engineering, 203–18. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3690-5_18.

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Kim, Ue-Hwan, Gyeong-Min Lee, and Jong-Hwan Kim. "Revisiting Self-supervised Monocular Depth Estimation." In Robot Intelligence Technology and Applications 6, 336–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97672-9_30.

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He, Mu, Le Hui, Yikai Bian, Jian Ren, Jin Xie, and Jian Yang. "RA-Depth: Resolution Adaptive Self-supervised Monocular Depth Estimation." In Lecture Notes in Computer Science, 565–81. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19812-0_33.

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Zhang, Min, and Jianhua Li. "Efficient Unsupervised Monocular Depth Estimation with Inter-Frame Depth Interpolation." In Lecture Notes in Computer Science, 729–41. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87361-5_59.

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Conference papers on the topic "Monocular depth"

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Lee, Jae-Han, and Chang-Su Kim. "Monocular Depth Estimation Using Relative Depth Maps." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.00996.

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Atapour-Abarghouei, Amir, and Toby P. Breckon. "Monocular Segment-Wise Depth: Monocular Depth Estimation Based on a Semantic Segmentation Prior." In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2019. http://dx.doi.org/10.1109/icip.2019.8803551.

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Dimiccoli, Mariella, Jean-Michel Morel, and Philippe Salembier. "Monocular Depth by Nonlinear Diffusion." In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icvgip.2008.97.

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Watson, Jamie, Michael Firman, Gabriel Brostow, and Daniyar Turmukhambetov. "Self-Supervised Monocular Depth Hints." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00225.

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"MONOCULAR DEPTH-BASED BACKGROUND ESTIMATION." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003816503230328.

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Dukor, Obumneme Stanley, S. Mahdi H. Miangoleh, Mahesh Kumar Krishna Reddy, Long Mai, and Yağız Aksoy. "Interactive Editing of Monocular Depth." In SIGGRAPH '22: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3532719.3543235.

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Civera, Javier, Andrew J. Davison, and J. M. M. Montiel. "Inverse Depth to Depth Conversion for Monocular SLAM." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.363892.

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Kamath K.M., Shreyas, Srijith Rajeev, Karen Panetta, and Sos S. Agaian. "DTTNet: Depth transverse transformer network for monocular depth estimation." In Multimodal Image Exploitation and Learning 2022, edited by Sos S. Agaian, Sabah A. Jassim, Stephen P. DelMarco, and Vijayan K. Asari. SPIE, 2022. http://dx.doi.org/10.1117/12.2618535.

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Yue, Haosong, Jinqing Zhang, Xingming Wu, Jianhua Wang, and Weihai Chen. "Edge Enhancement in Monocular Depth Prediction." In 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2020. http://dx.doi.org/10.1109/iciea48937.2020.9248336.

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Zhang, Ji, Michael Kaess, and Sanjiv Singh. "Real-time depth enhanced monocular odometry." In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014). IEEE, 2014. http://dx.doi.org/10.1109/iros.2014.6943269.

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Reports on the topic "Monocular depth"

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Winterbottom, Marc D., Robert Patterson, Byron J. Pierce, Christine Covas, and Jennifer Winner. The Influence of Depth of Focus on Visibility of Monocular Head-Mounted Display Symbology in Simulation and Training Applications. Fort Belvoir, VA: Defense Technical Information Center, February 2007. http://dx.doi.org/10.21236/ada464044.

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