Super 4PCS fast global pointcloud registration via smart indexing

Nicolas Mellado, Dror Aiger, Niloy J. Mitra

Research output: Contribution to journalArticlepeer-review

257 Scopus citations

Abstract

Data acquisition in large-scale scenes regularly involves accumulating information across multiple scans. A common approach is to locally align scan pairs using Iterative Closest Point (ICP) algorithm (or its variants), but requires static scenes and small motion between scan pairs. This prevents accumulating data across multiple scan sessions and/or different acquisition modalities (e.g., stereo, depth scans). Alternatively, one can use a global registration algorithm allowing scans to be in arbitrary initial poses. The state-of-the-art global registration algorithm, 4PCS, however has a quadratic time complexity in the number of data points. This vastly limits its applicability to acquisition of large environments. We present Super 4PCS for global pointcloud registration that is optimal, i.e., runs in linear time (in the number of data points) and is also output sensitive in the complexity of the alignment problem based on the (unknown) overlap across scan pairs. Technically, we map the algorithm as an 'instance problem' and solve it efficiently using a smart indexing data organization. The algorithm is simple, memory-efficient, and fast. We demonstrate that Super 4PCS results in significant speedup over alternative approaches and allows unstructured efficient acquisition of scenes at scales previously not possible. Complete source code and datasets are available for research use at http://geometry.cs. ucl.ac.uk/projects/2014/super4PCS/.

Original languageEnglish (US)
Pages (from-to)205-215
Number of pages11
JournalComputer Graphics Forum
Volume33
Issue number5
DOIs
StatePublished - Aug 2014

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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