  We introduce an innovative method for incremental nonparametric probabilistic
inference in high-dimensional state spaces. Our approach leverages \slices from
high-dimensional surfaces to efficiently approximate posterior distributions of
any shape. Unlike many existing graph-based methods, our \slices perspective
eliminates the need for additional intermediate reconstructions, maintaining a
more accurate representation of posterior distributions. Additionally, we
propose a novel heuristic to balance between accuracy and efficiency, enabling
real-time operation in nonparametric scenarios. In empirical evaluations on
synthetic and real-world datasets, our \slices approach consistently
outperforms other state-of-the-art methods. It demonstrates superior accuracy
and achieves a significant reduction in computational complexity, often by an
order of magnitude.
