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Object detection and classification from large‐scale cluttered indoor scans
Author(s) -
Mattausch Oliver,
Panozzo Daniele,
Mura Claudio,
SorkineHornung Olga,
Pajarola Renato
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12286
Subject(s) - point cloud , computer science , artificial intelligence , computer vision , segmentation , similarity (geometry) , preprocessor , visualization , object (grammar) , pattern recognition (psychology) , embedding , filter (signal processing) , representation (politics) , image (mathematics) , politics , political science , law
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.