Detecting and Parsing Architecture at City Scale from Range Data

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Computer Sciences
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Toshev, Alexander
Mordohai, Philippos
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We present a method for detecting and parsing buildings from unorganized 3D point clouds into a compact, hierarchical representation that is useful for high-level tasks. The input is a set of range measurements that cover large-scale urban environment. The desired output is a set of parse trees, such that each tree represents a semantic decomposition of a building – the nodes are roof surfaces as well as volumetric parts inferred from the observable surfaces. We model the above problem using a simple and generic grammar and use an efficient dependency parsing algorithm to generate the desired semantic description. We show how to learn the parameters of this simple grammar in order to produce correct parses of complex structures. We are able to apply our model on large point clouds and parse an entire city.

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2010-06-01
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2023-05-17T07:07:36.000
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Toshev, A.; Mordohai, P.; Taskar, B.; , "Detecting and parsing architecture at city scale from range data," Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on , vol., no., pp.398-405, 13-18 June 2010 doi: 10.1109/CVPR.2010.5540187 © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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