Object detection and classification from large-scale cluttered indoor scans

Oliver Mattausch, Daniele Panozzo, Claudio Mura, Olga Sorkine-Hornung, Renato Pajarola

Research output: Contribution to journalArticlepeer-review


We present a method to automatically segment indoor scenes by detecting repeated objects. Our algorithm scales to datasets with 198 million points and does not require any training data. We propose a trivially parallelizable preprocessing step, which compresses a point cloud into a collection of nearly-planar patches related by geometric transformations. This representation enables us to robustly filter out noise and greatly reduces the computational cost and memory requirements of our method, enabling execution at interactive rates. We propose a patch similarity measure based on shape descriptors and spatial configurations of neighboring patches. The patches are clustered in a Euclidean embedding space based on the similarity matrix to yield the segmentation of the input point cloud. The generated segmentation can be used to compress the raw point cloud, create an object database, and increase the clarity of the point cloud visualization.

Original languageEnglish (US)
Pages (from-to)11-21
Number of pages11
JournalComputer Graphics Forum
Issue number2
StatePublished - May 2014

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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