The Role of Cyclopean-Eye in Stereo Vision

2025-06-25

AI Summary

This work addresses the challenge of depth reconstruction in stereo vision under occlusions and depth discontinuities. We propose a novel method that integrates geometric priors with human-inspired perceptual mechanisms. Inspired by the Cyclopean Eye model, we design a geometry-constrained attention mechanism that explicitly models the intrinsic relationship between feature matching quality and 3D surface structure. Strong geometric constraints—including epipolar geometry and disparity smoothness—are embedded into a deep learning framework via principled theoretical derivation and validated on real-world datasets (SceneFlow, KITTI). Experiments demonstrate that our model reduces estimation error by 12.7%–18.3% at occlusion boundaries and depth discontinuities, outperforming state-of-the-art purely data-driven or weakly geometry-guided approaches. The method establishes a new paradigm for interpretable and robust stereo matching by unifying explicit geometric reasoning with deep representation learning.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Investigates geometric foundations of stereo vision systems
• Proposes novel constraints for occlusions and depth discontinuities
• Evaluates deep learning-based stereo feature matching quality

Innovation

• Novel geometric constraints for occlusions
• Deep learning for stereo feature matching
• Attention mechanisms for 3D surface recovery