paper-with-me

홈 › Papers

A single coordinate framework for optic flow and binocular disparity

2018-08-11

Optic flow is two dimensional, but no special qualities are attached to one or other of these dimensions. For binocular disparity, on the other hand, the terms 'horizontal' and 'vertical' disparities are commonly used. This is odd, since binocular disparity and optic flow describe essentially the same thing. The difference is that, generally, people tend to fixate relatively close to the direction of heading as they move, meaning that fixation is close to the optic flow epipole, whereas, for binocular vision, fixation is close to the head-centric midline, i.e. approximately 90 degrees from the binocular epipole. For fixating animals, some separations of flow may lead to simple algorithms for the judgement of surface structure and the control of action. We consider the following canonical flow patterns that sum to produce overall flow: (i) 'towards' flow, the component of translational flow produced by approaching (or retreating from) the fixated object, which produces pure radial flow on the retina; (ii) 'sideways' flow, the remaining component of translational flow, which is produced by translation of the optic centre orthogonal to the cyclopean line of sight and (iii) 'vergence' flow, rotational flow produced by a counter-rotation of the eye in order to maintain fixation. A general flow pattern could also include (iv) 'cyclovergence' flow, produced by rotation of one eye relative to the other about the line of sight. We consider some practical advantages of dividing up flow in this way when an observer fixates as they move. As in some previous treatments, we suggest that there are certain tasks for which it is sensible to consider 'towards' flow as one component and 'sideways' + 'vergence' flow as another.

📄 PDF Abstract BibTeX arXiv:1808.03875

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Neural Image Representations for Multi-Image Fusion and Layer Separation

2021-08-02 · Seonghyeon Nam, Marcus A. Brubaker, Michael S. Brown

We propose a framework for aligning and fusing multiple images into a single view using neural image representations (NIRs), also known as implicit or coordinate-based neural representations. Our framework targets burst …

Optical Flow Estimation

Optical Flow from Photons

2026-08-01 · Wendi Liu, Weichao Zeng, Weihang Ran, Yujie Lu 외 arxiv

Optical flow remains challenging in high-speed and low-light scenes, where the limited frame rate and sensitivity of conventional cameras lead to motion blur and underexposure. Single-photon avalanche diode (SPAD) camera…

Convolutional Neural Networks: A Binocular Vision Perspective

2019-12-21 · Yigit Oktar, Diclehan Karakaya, Oguzhan Ulucan, Mehmet Turkan

It is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the…

Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation

2022-03-04 · Naye Ji, Fan Zhang, Haoxiang Zhang, Youbing Zhao 외

Mixed reality applications often require virtual objects that are partly occluded by real objects. However, previous research and commercial products have limitations in terms of performance and efficiency. To address th…

GPUMixed RealityOptical Flow EstimationStereo Matching

Joint Discrete-Continuous Flow Matching for Open-Vocabulary Inverse Design of Multilayer Optical Coatings

2026-07-09 · Zhiyi Li, Yuheng Jin, Yidan Huang, Nan Chen 외 arxiv

Amortized neural inverse design typically remains closed-world: component choices are fixed vocabulary tokens, coordinate grids are frozen at training time, and continuous variables are discretized into sequence tokens. …