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  "path": "/abs/2602.18653v1",
  "publishedAt": "2026-02-24T01:00:00.000Z",
  "site": "https://arxiv.org",
  "tags": [
    "Haitao Wang"
  ],
  "textContent": "**Authors:** Haitao Wang\n\nChan [JACM, 2010] gave a data structure for maintaining the convex hull of a dynamic set of 3D points under insertions and deletions, supporting extreme-point queries. Subsequent refinements by Kaplan, Mulzer, Roditty, Seiferth, and Sharir [DCG, 2020] and Chan [DCG, 2020] preserved the framework while improving bounds. The current best result achieves $O(\\log^2 n)$ amortized insertion time, $O(\\log^4 n)$ amortized deletion time, and $O(\\log^2 n)$ worst-case query time. These techniques also yield dynamic 2D Euclidean nearest neighbor searching via duality, where the problem becomes maintaining the lower envelope of 3D planes for vertical ray-shooting queries. Using randomized vertical shallow cuttings, Kaplan et al. [DCG, 2020] and Liu [SICOMP, 2022] extended the framework to dynamic lower envelopes of general 3D surfaces, obtaining the same asymptotic bounds and enabling nearest neighbor searching under general distance functions. We revisit Chan's framework and present a modified structure that reduces the deletion time to $O(\\log^3 n \\log\\log n)$, while retaining $O(\\log^2 n)$ amortized insertion time, at the cost of increasing the query time to $O(\\log^3 n / \\log\\log n)$. When the overall complexity is dominated by deletions, this yields an improvement of roughly a logarithmic factor over previous results.",
  "title": "Dynamic 3D Convex Hulls Revisited and Applications"
}