Embedded Voxel Colouring with Adaptive Threshold Selection Using Globally Minimal Surfaces

  • Authors:

  • Carlos Leung;Ben Appleton;Mitchell Buckley;Changming Sun

  • Affiliations:

  • Intelligent Real-Time Imaging and Sensing Group, ITEE, The University of Queensland, Brisbane, Australia 4072 and Suncorp, Melbourne, Australia;Google Inc., Sydney, Australia;CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia 1670 and Macquarie University, Sydney, Australia;CSIRO Mathematics, Informatics and Statistics, North Ryde, Australia 1670

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2012

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Abstract

Image-based 3D reconstruction remains a competitive field of research as state-of-the-art algorithms continue to improve. This paper presents a voxel-based algorithm that adapts the earliest space-carving methods and utilises a minimal surface technique to obtain a cleaner result. Embedded Voxel Colouring is built in two stages: (a) progressive voxel carving is used to build a volume of embedded surfaces and (b) the volume is processed to obtain a surface that maximises photo-consistency data in the volume. This algorithm combines the strengths of classical carving techniques with those of minimal surface approaches. We require only a single pass through the voxel volume, this significantly reduces computation time and is the key to the speed of our approach. We also specify three requirements for volumetric reconstruction: monotonic carving order, causality of carving and water-tightness. Experimental results are presented that demonstrate the strengths of this approach.