Stereopsis

Summary

Stereopsis () is the component of depth perception retrieved through binocular vision. Stereopsis is not the only contributor to depth perception, but it is a major one. Binocular vision happens because each eye receives a different image because they are in slightly different positions on one's head (left and right eyes). These positional differences are referred to as "horizontal disparities" or, more generally, "binocular disparities". Disparities are processed in the visual cortex of the brain to yield depth perception. While binocular disparities are naturally present when viewing a real three-dimensional scene with two eyes, they can also be simulated by artificially presenting two different images separately to each eye using a method called stereoscopy. The perception of depth in such cases is also referred to as "stereoscopic depth". The perception of depth and three-dimensional structure is, however, possible with information visible from one eye alo

Official source

https://en.wikipedia.org/wiki/Stereopsis

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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

Stereo Vision Matching using Characteristics Vectors

Jean-Philippe Thiran

Stereo vision is a usual method to obtain depth information from images. The problems encountered when applying the majority of well established algorithms to provide this information are due to the high computational load required. This occurs in both the block matching and graphical cues (such as edges) matching. In this article we address this issue by performing an image analysis which considers each pixel only once, thus enhancing the efficiency of the image processing. Additionally, when matching is carried out over statistical descriptors of the image regions, commonly referred to as characteristic vectors, whose number of these vectors is, by definition, lower than the possible block matching possibilities, the algorithm achieves an improved level of performance. In this paper we present a new algorithm which has been specifically designed to solve the commonly observed problems which arise from other well know techniques. This algorithm was designed using a previous work carried out by the authors in this area to determine the descriptors extraction processes. The complete analysis has been carried out over gray scale images. The results obtained from both real and synthetic images are presented in terms of matching quality and time consumption and compared to other published results. Finally, a discussion is provided on additional features related to the matching process.

2010

Segment-Based Real-Time Stereo Vision Matching using Characteristics Vectors

Jean-Philippe Thiran

Stereo vision is a usual method to obtain depth information from images. The problems encountered when applying well established algorithms to real-time applications are due to the high computational load required. In this article we address this issue by performing a region-based analysis which considers each pixel only once. Additionally, matching is carried out over statistical descriptors of the image regions. In this paper we present a new algorithm that has been specifically designed to solve some commonly observed problems which arise from other well known techniques. This algorithm was designed using a previous algorithm implemented by the authors. The complete analysis has been carried out over gray scale images. The results obtained from both real and synthetic images are presented in terms of matching quality and time consumption and are compared to other published results. Finally, a discussion on additional features related to the matching process is provided.

2011