Details
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2896-2904 |
| Number of pages | 9 |
| ISBN (electronic) | 9781728125060 |
| ISBN (print) | 9781728125077 |
| Publication status | Published - 2019 |
| Event | 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019 - Long Beach, United States Duration: 16 Jun 2019 → 20 Jun 2019 |
Publication series
| Name | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
|---|---|
| Volume | 2019-June |
| ISSN (Print) | 2160-7508 |
| ISSN (electronic) | 2160-7516 |
Abstract
Recently, global SfM has been attracting many researchers, mainly because of its time efficiency. Most of these methods are based on averaging relative orientations (ROs). Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global SfM. In this paper, we propose a method to eliminate wrong ROs which have resulted from repetitive structure (RS) and very short baselines (VSB). We suggest two corresponding criteria that indicate the quality of ROs. These criteria are functions of potentially conjugate points resulting from local image matching of image pairs, followed by a geometry check using the 5-point algorithm combined with RANSAC. RS is detected based on counts of corresponding conjugate points of the various pairs, while VSB is found by inspecting the intersection angles of corresponding image rays. Based on these two criteria, incorrect ROs are eliminated. We demonstrate the proposed method on various datasets by inserting our refined ROs into a global SfM pipeline. The experiments show that compared to other methods we can generate the better results in this way.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Electrical and Electronic Engineering
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2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Institute of Electrical and Electronics Engineers Inc., 2019. p. 2896-2904 9025434 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2019-June).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Robustifying relative orientations with respect to repetitive structures and very short baselines for global SfM
AU - Wang, Xin
AU - Xiao, Teng
AU - Gruber, Michael
AU - Heipke, Christian
N1 - Funding Information: Parts of works were done at Vexcel Imaging GmbH, Graz, financially supported by the EU project “innoVation in geOspatiaL and 3D daTA — VOLTA” funded under the Marie-Curie RISE scheme as no. 734687. The author Xin Wang would like to thank the China Scholarship Council (CSC) for financially supporting his PhD studying at Leibniz Universität Hannover, Germany. The author Xiao Teng would like to thank the Graduate Student Exchange Program of Wuhan University for his visiting scientist scholarship for studying in Germany.
PY - 2019
Y1 - 2019
N2 - Recently, global SfM has been attracting many researchers, mainly because of its time efficiency. Most of these methods are based on averaging relative orientations (ROs). Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global SfM. In this paper, we propose a method to eliminate wrong ROs which have resulted from repetitive structure (RS) and very short baselines (VSB). We suggest two corresponding criteria that indicate the quality of ROs. These criteria are functions of potentially conjugate points resulting from local image matching of image pairs, followed by a geometry check using the 5-point algorithm combined with RANSAC. RS is detected based on counts of corresponding conjugate points of the various pairs, while VSB is found by inspecting the intersection angles of corresponding image rays. Based on these two criteria, incorrect ROs are eliminated. We demonstrate the proposed method on various datasets by inserting our refined ROs into a global SfM pipeline. The experiments show that compared to other methods we can generate the better results in this way.
AB - Recently, global SfM has been attracting many researchers, mainly because of its time efficiency. Most of these methods are based on averaging relative orientations (ROs). Therefore, eliminating incorrect ROs is of great significance for improving the robustness of global SfM. In this paper, we propose a method to eliminate wrong ROs which have resulted from repetitive structure (RS) and very short baselines (VSB). We suggest two corresponding criteria that indicate the quality of ROs. These criteria are functions of potentially conjugate points resulting from local image matching of image pairs, followed by a geometry check using the 5-point algorithm combined with RANSAC. RS is detected based on counts of corresponding conjugate points of the various pairs, while VSB is found by inspecting the intersection angles of corresponding image rays. Based on these two criteria, incorrect ROs are eliminated. We demonstrate the proposed method on various datasets by inserting our refined ROs into a global SfM pipeline. The experiments show that compared to other methods we can generate the better results in this way.
UR - http://www.scopus.com/inward/record.url?scp=85083328892&partnerID=8YFLogxK
U2 - 10.1109/CVPRW.2019.00349
DO - 10.1109/CVPRW.2019.00349
M3 - Conference contribution
AN - SCOPUS:85083328892
SN - 9781728125077
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2896
EP - 2904
BT - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019
Y2 - 16 June 2019 through 20 June 2019
ER -