We propose SteinPnP, a probabilistic Perspective- N-Point (PnP) solver that models the full 6-DoF camera pose posterior using a non-parametric set of pose particles. By lever- aging Stein Variational Inference (SVI), SteinPnP combines the expressivity of particle-based representations with the efficiency of optimization-based solvers. However, a naive application of SVI struggles in the PnP problem, often missing alternative pose hypotheses or assigning incorrect probability weights to them. To address these limitations, we propose practical remedies that discover new potential poses, identify distinct hypothesis clusters, and balance their relative probability weights to capture the true underlying distribution. Our experiments across synthetic and real-world benchmarks demonstrate that SteinPnP accurately captures complex multimodal uncertainties while maintaining competitive accuracy.

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