The continuous Fréchet distance 𝒟F (π, σ) between two polygonal curves π and σ is classically computed by exploring the free space diagram over the two curves. SoCG'25 recently proposed a radically different approach: they approximate 𝒟F (π, σ) by computing paths in a discrete graph that models a joint traversal of π and σ, recursively bisecting edges until the discrete distance converges to the continuous one. They implement their "frog-based" technique, and claim that it yields substantial practical speedups compared to the state-of-the-art implementations. In this paper, we revisit this technique. We observe that, in its current form, it has three limitations: (i) it does not use exact arithmetic, (ii) its recursive bisection introduces the required monotonicity events to realise the Fréchet distance only in the limit, and (iii) it applies a heuristic simplification technique which is overly conservative. Motivated by theoretical interest, we develop new techniques that guarantee exactness, polynomial-time convergence and near-optimal lossless simplifications. We provide an open-source C++ implementation of our variant. Our primary contribution is an extensive empirical evaluation on a broad, publically available, suite of real-world and synthetic data sets. Among the frog-based variants, exact computation indeed introduces overhead and increases median runtime. Yet, our new approach is often faster in the worst case, worst ten percent, or even the average runtime due to its worst-case convergence guarantees. More surprisingly, the implementation of SoCG'19 dominates all frog-based implementations in performance - this finding contrasts previously published claims. These results provide a much-needed nuanced perspective on the capabilities and limitations of frog-based techniques: we showcase its theoretical appeal, but highlight its limited practical feasibility.
Conradi et al. (2026) studied this question.