Autonomous Stereo Vision Parameter Optimization based on Nonlinear Conjugate Gradient

Stereo vision systems serve as a cornerstone for depth perception in autonomous robotics, advanced driver-assistance systems, and three-dimensional reconstruction. However, the performance of stereo matching algorithms is heavily contingent upon the precise tuning of hyperparameters, such as penalty coefficients, window sizes, and aggregation thresholds. Manual tuning of these parameters is laborious, subjective, and often fails to generalize across diverse environmental conditions. This paper introduces an automated framework for optimizing stereo vision parameters utilizing the Nonlinear Conjugate Gradient (NCG) method. By formulating the parameter selection process as a continuous optimization problem within a multidimensional cost landscape, we employ NCG to efficiently navigate towards optimal configurations. Unlike stochastic methods such as genetic algorithms, NCG leverages gradient approximation to achieve faster convergence while maintaining high solution quality. We validate our approach using standard benchmark datasets, demonstrating that the NCG-optimized parameters significantly reduce disparity error rates compared to baseline configurations and perform competitively against computationally expensive global search methods. The proposed framework offers a robust, time-efficient solution for adaptive stereo vision in dynamic environments.