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Object detection and classification from large-scale cluttered indoor scans


Mattausch, Oliver; Panozzo, Daniele; Mura, Claudio; Sorkine-Hornung, Olga; Pajarola, R (2014). Object detection and classification from large-scale cluttered indoor scans. Computer Graphics Forum, 33(2):11-21.

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.

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.

Citations

11 citations in Web of Science®
14 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:graphics, architecture, 3D reconstruction, point cloud, scanning, indoor scene reconstruction, segmentation
Language:English
Date:2014
Deposited On:22 Jan 2015 15:36
Last Modified:05 Apr 2016 18:46
Publisher:The Eurographics Association and John Wiley & Sons Ltd.
Publisher DOI:https://doi.org/10.1111/cgf.12286
Official URL:http://onlinelibrary.wiley.com/doi/10.1111/cgf.12286/abstract
Other Identification Number:merlin-id:10252

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