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3D Semantic Parsing of Large-Scale Indoor Spaces

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Armeni, Iro 
Sener, Ozan 
Zamir, Amir R 
Jiang, Helen 

Abstract

In this paper, we propose a method for semantic parsing the 3D point cloud of an entire building using a hierarchical approach: first, the raw data is parsed into semantically meaningful spaces (e.g. rooms, etc) that are aligned into a canonical reference coordinate system. Second, the spaces are parsed into their structural and building elements (e.g. walls, columns, etc). Performing these with a strong notation of global 3D space is the backbone of our method. The alignment in the first step injects strong 3D priors from the canonical coordinate system into the second step for dis-covering elements. This allows diverse challenging scenarios as man-made indoor spaces often show recurrent geo-metric patterns while the appearance features can change drastically. We also argue that identification of structural elements in indoor spaces is essentially a detection problem, rather than segmentation which is commonly used. We evaluated our method on a new dataset of several buildings with a covered area of over6,000m2and over215millionpoints, demonstrating robust results readily useful for practical applications.

Description

Keywords

4013 Geomatic Engineering, 46 Information and Computing Sciences, 40 Engineering

Journal Title

2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)

Conference Name

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Journal ISSN

1063-6919

Volume Title

Publisher

IEEE
Sponsorship
Engineering and Physical Sciences Research Council (EP/I019308/1)
Engineering and Physical Sciences Research Council (EP/K000314/1)
Engineering and Physical Sciences Research Council (EP/L010917/1)
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