提出新几何约束，提升立体视觉深度重建精度
The Role of Cyclopean-Eye in Stereo Vision
- 引入人类感知启发的环形眼模型，建模遮挡与深度不连续性
- 实验证明结合几何先验与深度学习特征可显著提升匹配质量
- 适合关注立体视觉机制与3D重建的计算机视觉研究者
本文研究现代立体视觉系统的几何基础，聚焦三维结构与类人感知如何促进精确深度重建。重新审视环形眼模型，提出新的几何约束以处理遮挡和深度不连续问题。分析基于深度学习模型的立体特征匹配质量，以及注意力机制在恢复有意义三维表面中的作用。通过理论推导与真实数据集上的实证研究，证明将强几何先验与学习特征结合，能为理解立体视觉系统提供内部表征。
原文摘要 · Abstract (English)
This work investigates the geometric foundations of modern stereo vision systems, with a focus on how 3D structure and human-inspired perception contribute to accurate depth reconstruction. We revisit the Cyclopean Eye model and propose novel geometric constraints that account for occlusions and depth discontinuities. Our analysis includes the evaluation of stereo feature matching quality derived from deep learning models, as well as the role of attention mechanisms in recovering meaningful 3D surfaces. Through both theoretical insights and empirical studies on real datasets, we demonstrate that combining strong geometric priors with learned features provides internal abstractions for understanding stereo vision systems.
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