Radiance Field-Based Pose Estimation via Decoupled Optimization Under Challenging Initial Conditions

Estimating six-degree-of-freedom poses is essential but difficult, especially under challenging initial conditions, as existing methods relying on photometric loss often fail catastrophically. To address this, we propose a novel radiance field-based pose estimation framework that combines Monte Carlo initialization and decoupled optimization. First, we design an initialization stage based on Monte Carlo sampling, which screens the current optimal initial pose by generating multiple pose hypotheses within the known scene space. Subsequently, our decoupled optimization strategy independently refines the rotational and translational components, significantly improving the motion stability of the camera during the convergence process. Comprehensive evaluations on synthetic and real-world datasets demonstrate that our method outperforms existing approaches in terms of pose regression accuracy and robustness, even under challenging initial pose.