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Even Faster Algorithm for the Chamfer Distance

Authors Ying Feng, Piotr Indyk

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  • Theory of computation
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  • Chamfer distance

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Abstract

For two d-dimensional point sets A,B of size up to n, the Chamfer distance from A to B is defined as CH(A,B) = ∑_{a ∈ A} min_{b ∈ B} ‖a-b‖. The Chamfer distance is a widely used measure for quantifying dissimilarity between sets of points, used in many machine learning and computer vision applications. A recent work of Bakshi et al, NeuriPS'23, gave the first near-linear time (1+ε)-approximate algorithm, with a running time of 𝒪(nd log (n)/ε²). In this paper we improve the running time further, to 𝒪(nd(log log n+log1/(ε))/ε²)). When ε is a constant, this reduces the gap between the upper bound and the trivial Ω(dn) lower bound significantly, from 𝒪(log n) to 𝒪(log log n).

Cite As Get BibTex

Ying Feng and Piotr Indyk. Even Faster Algorithm for the Chamfer Distance. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 76:1-76:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICALP.2025.76

Author Details

Ying Feng
  • MIT, Cambridge, MA, USA
Piotr Indyk
  • MIT, Cambridge, MA, USA

Funding

  • Feng, Ying: Supported by an MIT Akamai Presidential Fellowship.
  • Indyk, Piotr: Supported in part by the NSF TRIPODS program (award DMS-2022448).

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