journal = {Computer Graphics Forum},
title = {Object Detection and Classification from Large-Scale Cluttered Indoor Scans},
author = {Oliver Mattausch and Daniele Panozzo and Claudio Mura and Olga Sorkine-Hornung and 
Renato Pajarola },
year = {2014},
pages = {11-21},
volume= {33},
number= {2},
URL = {http://diglib.eg.org/EG/CGF/volume33/issue2/v33i2pp011-021.pdf},
DOI = {10.1111/cgf.12286},
keywords = {symmetry detection, indoor reconstruction}
abstract = {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.}