Perception de la profondeur

Résumé

La perception de la profondeur est la capacité visuelle à percevoir le monde en trois dimensions. La perception de la profondeur et du relief se base sur différents types d'indices visuels qu'il est possible de classer en trois catégories : en premier lieu ceux qui dépendent du mouvement ; parmi les autres, ceux qui requièrent la vision binoculaire ; enfin, ceux perçus avec un seul œil. Les objets de l'environnement présentent en général plusieurs indices convergents sur leur position dans la profondeur de l'espace. La psychologie expérimentale met au point des expériences avec des motifs simplifiés, qui n'en présentent qu'un à la fois, et des illusions visuelles, où ces indices, contradictoires, provoquent une interprétation hésitante ou aberrante. Mouvement Parallaxe vignette|La variation de parallaxe crée une notion du relief. L'être humain n'est jamais parfaitement immobile. Lorsqu'il bouge, les objets proches se déplacent plus dans l'appareil visuel que le

Source officielle

https://fr.wikipedia.org/wiki/Perception_de_la_profondeur

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Depth-Averaged Material Point Method for the Release of Slab Avalanches

Louis Guillet

2021

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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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Camera Tracking using natural feature point matching for Augmented Reality

Touradj Ebrahimi, Cecilia Torralbo Gimeno

For augmented reality applications, accurate estimation of the camera pose is required. An existing video-based marker tracking system developed at the ITS presents some limitations when the marker is completely or partially occluded. The objective of this project is to improve the tracking of the camera on those cases. For this purpose, the tracking of the marker is extended to that of natural feature points around the marker's environment. The extension to natural features works as follows. Feature point candidates are being searched in the image. Depth estimation is implemented in a recursive method triangulating points between the first frame and subsequent frames. A particle filter is used to take into account the new measures received with every frame. Particle's weights are sensible to the state of the camera tracker and the proximity of the triangulated point to the expected depth. Eventually the distribution of the depth particle filter for a given feature converges at one point. This position is added to a map of feature points used to update the camera pose. Results show that tracking was improved. The camera pose was tracked even though the marker was not visible. However a small drift in its pose is detected due to the imprecision of feature point positions. This drift is not excessive in the sense that the tracker is able to recover the right camera pose. Future work will focus on improving the accuracy of the depth estimates.

2007

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