$\pi$-BA: Probabilistic Neural Bundle Adjustment With Iterative Cycle Optimization for Driving Scene Reconstruction

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这篇工作首先强调：Urban scene reconstruction under noisy camera poses remains a critical challenge for autonomous driving.

对机器人系统设计、实验选题和工程落地都有一定参考价值。

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理论线索

方法线上更接近 约束建模 / 几何/深度估计。While recent neural dense Bundle Adjustment (BA) methods have shown promising results in specific settings, their performance often degrades in real-world urban scenarios due to noisy correspondences and imbalanced optimization between camera poses and scene parameters, which leads the scene representation to overfit to erroneous geometric constraints, causing the system to converge to suboptimal local minima.