用神经几何模型实现三维视觉的感知单元分割，提升立体匹配精度。
Individuation of 3D perceptual units from neurogeometry of binocular cells
- 基于子黎曼几何建模双目神经元功能结构
- 通过调和分析实现局部匹配与全局感知单元整合
- 适合计算机视觉与神经科学交叉研究者阅读
我们通过扩展 \\(\cite{BCSZ23}\\) 提出的立体视觉神经几何子黎曼模型，构建了早期三维视觉的功能架构。提出一种新对应框架，结合神经算法实现局部立体匹配，同时将对应点组织为全局感知单元，从而实现有效场景分割。该方法基于子黎曼结构上的调和分析，对比黎曼距离表明，子黎曼度量在解决方案中起核心作用。
原文摘要 · Abstract (English)
We model the functional architecture of the early stages of three-dimensional vision by extending the neurogeometric sub-Riemannian model for stereo-vision introduced in \cite{BCSZ23}. A new framework for correspondence is introduced that integrates a neural-based algorithm to achieve stereo correspondence locally while, simultaneously, organizing the corresponding points into global perceptual units. The result is an effective scene segmentation. We achieve this using harmonic analysis on the sub-Riemannian structure and show, in a comparison against Riemannian distance, that the sub-Riemannian metric is central to the solution.
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