NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS
This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally construct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid method between a surface-based a...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/11/2016/isprs-archives-XLI-B3-11-2016.pdf |
| _version_ | 1818975629076332544 |
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| author | K. O. Dierenbach M. Weinmann B. Jutzi |
| author_facet | K. O. Dierenbach M. Weinmann B. Jutzi |
| author_sort | K. O. Dierenbach |
| collection | DOAJ |
| description | This work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally
construct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid
method between a surface-based and a volumetric approach with a continuous model space. Hence, a new scan taken from an optimal
position should either cover as much as possible from the unknown object surface in one single scan, or densify the existing data and
close possible gaps. Based on the point density, we recover the essential and structural information of a scene based on the Growing
Neural Gas (GNG) algorithm. From the created graph representation of topological relations, the density of the point cloud at each
network node is estimated by approximating the volume of Voronoi cells. The NBV Finder selects a network node as NBV, which
has the lowest point density. Our NBV method is self-terminating when all regions reach a predefined minimum point density or the
change of the GNG error is zero. For evaluation, we use a Buddha statue with a rather simple surface geometry but still some concave
parts and the Stanford Dragon with a more complex object surface containing occluded and concave parts. We demonstrate that our
NBV method outperforms a “naive random” approach relying on uniformly distributed sensor positions in terms of efficiency, i.e. our
proposed method reaches a desired minimum point density up to 20% faster with less scans. |
| first_indexed | 2024-12-20T15:58:59Z |
| format | Article |
| id | doaj.art-931ebe89af914a1baa4c21162af7cb79 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-20T15:58:59Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-931ebe89af914a1baa4c21162af7cb792022-12-21T19:34:21ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B3111910.5194/isprs-archives-XLI-B3-11-2016NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONSK. O. Dierenbach0M. Weinmann1B. Jutzi2Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), GermanyInstitute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), GermanyInstitute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), GermanyThis work describes an iterative algorithm for estimating optimal viewpoints, so called next-best-views (NBVs). The goal is to incrementally construct a topological network from the scene during the consecutive acquisition of several views. Our approach is a hybrid method between a surface-based and a volumetric approach with a continuous model space. Hence, a new scan taken from an optimal position should either cover as much as possible from the unknown object surface in one single scan, or densify the existing data and close possible gaps. Based on the point density, we recover the essential and structural information of a scene based on the Growing Neural Gas (GNG) algorithm. From the created graph representation of topological relations, the density of the point cloud at each network node is estimated by approximating the volume of Voronoi cells. The NBV Finder selects a network node as NBV, which has the lowest point density. Our NBV method is self-terminating when all regions reach a predefined minimum point density or the change of the GNG error is zero. For evaluation, we use a Buddha statue with a rather simple surface geometry but still some concave parts and the Stanford Dragon with a more complex object surface containing occluded and concave parts. We demonstrate that our NBV method outperforms a “naive random” approach relying on uniformly distributed sensor positions in terms of efficiency, i.e. our proposed method reaches a desired minimum point density up to 20% faster with less scans.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/11/2016/isprs-archives-XLI-B3-11-2016.pdf |
| spellingShingle | K. O. Dierenbach M. Weinmann B. Jutzi NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS |
| title_full | NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS |
| title_fullStr | NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS |
| title_full_unstemmed | NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS |
| title_short | NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS |
| title_sort | next best view method based on consecutive evaluation of topological relations |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/11/2016/isprs-archives-XLI-B3-11-2016.pdf |
| work_keys_str_mv | AT kodierenbach nextbestviewmethodbasedonconsecutiveevaluationoftopologicalrelations AT mweinmann nextbestviewmethodbasedonconsecutiveevaluationoftopologicalrelations AT bjutzi nextbestviewmethodbasedonconsecutiveevaluationoftopologicalrelations |