View suggestion for interactive segmentation of indoor scenes

Abstract Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is ve...

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Main Authors: Sheng Yang, Jie Xu, Kang Chen, Hongbo Fu
Format: Article
Language:English
Published: SpringerOpen 2017-03-01
Series:Computational Visual Media
Subjects:
Online Access:http://link.springer.com/article/10.1007/s41095-017-0078-4
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author Sheng Yang
Jie Xu
Kang Chen
Hongbo Fu
author_facet Sheng Yang
Jie Xu
Kang Chen
Hongbo Fu
author_sort Sheng Yang
collection DOAJ
description Abstract Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming. In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods.
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spelling doaj.art-4bfb9b0e63a14ba0b0b207247c89ae9d2022-12-22T02:44:51ZengSpringerOpenComputational Visual Media2096-04332096-06622017-03-013213114610.1007/s41095-017-0078-4View suggestion for interactive segmentation of indoor scenesSheng Yang0Jie Xu1Kang Chen2Hongbo Fu3Tsinghua UniversityMassachusetts Institute of TechnologyTsinghua UniversityCity University of Hong KongAbstract Point cloud segmentation is a fundamental problem. Due to the complexity of real-world scenes and the limitations of 3D scanners, interactive segmentation is currently the only way to cope with all kinds of point clouds. However, interactively segmenting complex and large-scale scenes is very time-consuming. In this paper, we present a novel interactive system for segmenting point cloud scenes. Our system automatically suggests a series of camera views, in which users can conveniently specify segmentation guidance. In this way, users may focus on specifying segmentation hints instead of manually searching for desirable views of unsegmented objects, thus significantly reducing user effort. To achieve this, we introduce a novel view preference model, which is based on a set of dedicated view attributes, with weights learned from a user study. We also introduce support relations for both graph-cut-based segmentation and finding similar objects. Our experiments show that our segmentation technique helps users quickly segment various types of scenes, outperforming alternative methods.http://link.springer.com/article/10.1007/s41095-017-0078-4point cloud segmentationview suggestioninteractive segmentation
spellingShingle Sheng Yang
Jie Xu
Kang Chen
Hongbo Fu
View suggestion for interactive segmentation of indoor scenes
Computational Visual Media
point cloud segmentation
view suggestion
interactive segmentation
title View suggestion for interactive segmentation of indoor scenes
title_full View suggestion for interactive segmentation of indoor scenes
title_fullStr View suggestion for interactive segmentation of indoor scenes
title_full_unstemmed View suggestion for interactive segmentation of indoor scenes
title_short View suggestion for interactive segmentation of indoor scenes
title_sort view suggestion for interactive segmentation of indoor scenes
topic point cloud segmentation
view suggestion
interactive segmentation
url http://link.springer.com/article/10.1007/s41095-017-0078-4
work_keys_str_mv AT shengyang viewsuggestionforinteractivesegmentationofindoorscenes
AT jiexu viewsuggestionforinteractivesegmentationofindoorscenes
AT kangchen viewsuggestionforinteractivesegmentationofindoorscenes
AT hongbofu viewsuggestionforinteractivesegmentationofindoorscenes