Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

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Publication Details

Journal
Max Planck Institute for Plasma Physics
Published
2024-11-27
DOI
https://doi.org/10.20385/1860-2037/7.2010.2
Citations
6
Primary Topic
Advanced Vision and Imaging
Type
article
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article

Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

Michael Wand, Nils Hasler, Thorsten Thormählen, Hans‐Peter Seidel
6 citations
Max Planck Institute for Plasma Physics
Advanced Vision and Imaging
article

Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation

Michael Wand, Nils Hasler, Thorsten Thormählen, Hans‐Peter Seidel
article en
6 citations

Abstract

This paper presents different application scenarios for which the registration of sub-sequence reconstructions or multi-camera reconstructions is essential for successful camera motion estimation and 3D reconstruction from video. The registration is achieved by merging unconnected feature point tracks between the reconstructions. One application is drift removal for sequential camera motion estimation of long sequences. The state-of-the-art in drift removal is to apply a RANSAC approach to find unconnected feature point tracks. In this paper an alternative spectral algorithm for pairwise matching of unconnected feature point tracks is used. It is then shown that the algorithms can be combined and applied to novel scenarios where independent camera motion estimations must be registered into a common global coordinate system. In the first scenario multiple moving cameras, which capture the same scene simultaneously, are registered. A second new scenario occurs in situations where the tracking of feature points during sequential camera motion estimation fails completely, e.g., due to large occluding objects in the foreground, and the unconnected tracks of the independent reconstructions must be merged. In the third scenario image sequences of the same scene, which are captured under different illuminations, are registered. Several experiments with challenging real video sequences demonstrate that the presented techniques work in practice.

Max Planck Institute for Plasma PhysicsVol. 7(2)
Max Planck Society (DE), Max Planck Institute for Informatics (DE)
Openalex Percentile: Top 100%
Advanced Vision and Imaging
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Registration of Sub-Sequence and Multi-Camera Reconstructions for Camera Motion Estimation — Michael Wand, Nils Hasler, et al. · Max Planck Institute for Plasma Physics (2024) | TGRS Research Map | TGRS