2026-09-05T11:55:21Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:25813 2026-06-23T12:58:51Z ddc:004 open_access

Computing L_∞ Hausdorff Distances Under Translations: The Interplay of Dimensionality, Symmetry and Discreteness Angrick, Sebastian Buchin, Kevin Gokaj, Geri Künnemann, Marvin Hausdorff Distance Fine-Grained Complexity Computational Geometry Translation-Invariant Similarity Measures To measure the similarity of the shape of point sets, rather than their mere closeness in space, various notions of a Hausdorff distance under translation have been investigated. Specifically, let P and Q denote point sets of n and m points, respectively, in ℝ^d. We consider the task of computing the minimum distance d(P,Q+τ) over an admissible set of translations τ ∈ T, where d(⋅, ⋅) denotes the Hausdorff distance under the L_∞-norm. As variants, we distinguish between continuous (T = ℝ^d) or discrete (T is a given finite set of t translations) as well as directed or undirected (choosing the directed or undirected Hausdorff distance for d(⋅, ⋅)). We seek to apply the paradigm of fine-grained complexity to understand the complexity of these variants, and in particular: How is the running time influenced by the dimension d, the relationship between n and m, and the specific choice of variant? As our main results, we obtain: - The asymmetric definition of the most studied variant, the continuous directed Hausdorff distance, results in an intrinsically asymmetric time complexity: While (Chan, SoCG'23) established a symmetric Õ((nm)^{d/2}) upper bound for all d ≥ 3 and proved it to be conditionally optimal for combinatorial algorithms whenever m ≤ n, we show that this lower bound does not hold for the case n ≪ m, by providing a combinatorial, almost-linear-time algorithm for d = 3 and n = m^{o(1)}. We further prove general, i.e., non-combinatorial, conditional lower bounds for d ≥ 3, in particular: (1) m^{⌊d/2⌋ - o(1)} for small n and (2) n^{d/2 - o(1)} for d = 3 and small m. - We observe that the directed and undirected case is closely related, in particular, all our lower bounds for d ≥ 3 hold for both the directed and undirected variant. A remarkable exception is the case of d = 1 for which we provide a conditional separation. Specifically, in contrast to the undirected variants being solvable in near-linear time (Rote, IPL'91), we show that the directed variants are at least as hard as the additive problem MaxConv LowerBound introduced in (Cygan, Mucha, Wegrzycki and Wlodarczyk, TALG'19). - We show that the discrete variants reduce to a variant of 3SUM for d ≤ 3. This gives a barrier in proving a tight lower bound of these variants under the Orthogonal Vectors Hypothesis (OVH); in contrast, the continuous variants admit a tight conditional lower bound under OVH in d = 2 (Bringmann, Nusser, JoCG'21). These results reveal an intricate interplay of dimensionality, symmetry and discreteness in determining the fine-grained complexity of computing Hausdorff distances under translation. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sebastian Angrick and Kevin Buchin and Geri Gokaj and Marvin Künnemann 2026 Is Part Of LIPIcs, Volume 367, 42nd International Symposium on Computational Geometry (SoCG 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2026.7 urn:nbn:de:0030-drops-258131 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2026.7 eng https://creativecommons.org/licenses/by/4.0/legalcode