Online geometric reconstruction

  • Authors:
  • Bernard Chazelle;C. Seshadhri

  • Affiliations:
  • Princeton University, Princeton, NJ;Sandia National Labs, Livermore, Livermore, CA

  • Venue:
  • Journal of the ACM (JACM)
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

We investigate a new class of geometric problems based on the idea of online error correction. Suppose one is given access to a large geometric dataset though a query mechanism; for example, the dataset could be a terrain and a query might ask for the coordinates of a particular vertex or for the edges incident to it. Suppose, in addition, that the dataset satisfies some known structural property P (for example, monotonicity or convexity) but that, because of errors and noise, the queries occasionally provide answers that violate P. Can one design a filter that modifies the query's answers so that (i) the output satisfies P; (ii) the amount of data modification is minimized? We provide upper and lower bounds on the complexity of online reconstruction for convexity in 2D and 3D.