Stéréoscopie

Résumé

La stéréoscopie (du grec stéréo- : solide, -scope : vision) est l'ensemble des techniques mises en œuvre pour reproduire une perception du relief à partir de deux s planes. La stéréoscopie se base sur le fait que la perception humaine du relief se forme dans le cerveau lorsqu'il reconstitue une seule image à partir de la perception des deux images planes et différentes provenant de chaque œil. Il existe, pour réaliser ces images, aussi bien que pour les observer, une grande variété de moyens, à la description desquels plusieurs centaines de livres ont été consacrés. La désignation récente « film en 3D » est employée par anglicisme et par méconnaissance de la terminologie correcte : film stéréoscopique ou film en stéréoscopie. Historique Des travaux anciens annoncent l'apparition de la stéréoscopie au . Ainsi, un couple de dessins en stéréo du a été retrouvé dans une bibliothèque à Oxford. La collection Jean-Baptiste Wicar du Palais des beaux-arts de Lille conserve deux d

Source officielle

https://fr.wikipedia.org/wiki/St%C3%A9r%C3%A9oscopie

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Deep eyes: Joint depth inference using monocular and binocular cues

Siyuan Li

Human visual system relies on both monocular focusness cues and binocular stereo cues to gain effective 3D perception. Correspondingly, depth from focus/defocus (DfF/DfD) and stereo matching are two most studied passive depth sensing schemes, which are traditionally solved in separate tracks. However, the two techniques are essentially complementary: the monocular cue from DfF/DfD can robustly handle repetitive textures and occlusion that are problematic for stereo matching whereas the binocular cue from stereo matching is insensitive to defocus blurs and can resolve large depth range. In this paper, we emulate human perception and present unified learning-based techniques to conduct hybrid DfF/ DfD and stereo matching. We first construct a comprehensive focal stack dataset synthesized by depth-guided light field rendering. Next, we propose different network architectures to suit various inputs, including focal stack, stereo image pair, binocular focal stack, a focus-defocus image pair and defocus-stereo image triplet. We also exploit different connection methods between the separate net-works for integrating them into an optimized solution to produce high fidelity disparity maps. For exper-iment, we further explore different hardware setup to capture both monocular and binocular depth cues. Results show that our new learning-based hybrid techniques can significantly improve accuracy and robustness in depth estimation. (C) 2020 Elsevier B.V. All rights reserved.

ELSEVIER2021

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Stereoscopic video content is usually being created by using two or more cameras which are recording the same scene. Traditionally, those cameras have the exact same intrinsic camera parameters. In this project, the exposure times of the cameras differ, allowing to record different parts of the dynamic range of the scene. Image processing techniques are then used to enhance the dynamic range of the captured data. A pipeline for the recording, processing, and displaying of high dynamic range (HDR) stereoscopic content, acquired using inexpensive low dynamic range (LDR) cameras, is proposed. Two different approaches to obtain stereoscopic HDR content are presented and compared. In the temporal approach, different parts of the luminance range of the scene are recorded by temporally changing the exposure time of both cameras. Information from adjacent frames captured by the same camera is then used in order to increase the dynamic range. In the spatial approach, both cameras are assigned a distinct, fixed exposure time. Here, the dynamic range is increased by combining data from the cameras. It is found that the intrinsic problems of the spatial approach are much more difficult to deal with than the ones of the temporal approach. In particular stereo matching, the critical component to combine data in the spatial approach, is more difficult than traditionally because the two cameras have different exposure times. The results are evaluated for both static scenes and scenes with object movement using an objective quality metric of the visible differences of the stereoscopic pair independently, and visual evaluation on a stereoscopic display to evaluate the stereoscopic quality.

2011

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On the first JND and break in presence of 360-degree content

Roberto Gerson De Albuquerque Azevedo, Pascal Frossard

Unlike traditional planar 2D visual content, immersive 360-degree images and videos undergo particular processing steps and are intended to be consumed via head-mounted displays (HMDs). To get a deeper understanding on the perception of 360-degree visual distortions when consumed through HMDs, we perform an exploratory task-based subjective study in which we have asked subjects to define the first noticeable difference and break-in-presence points when incrementally adding specific compression artifacts. The results of our study: give insights on the range of allowed visual distortions for 360-degree content; show that the added visual distortions are more tolerable in mono than in stereoscopic 3D; and identify issues with current 360-degree objective quality metrics.

ACM Press2019

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