Compacting Singleshot Multi-Plane Image via Scale Adjustment

Larsson, Viktor (författare): Lund University,Lunds universitet,Mathematical Imaging Group,Forskargrupper vid Lunds universitet,Matematik LTH,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,LTH profilområde: AI och digitalisering,LTH profilområden,Lund University Research Groups,Mathematics (Faculty of Engineering),Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH,LTH Profile Area: AI and Digitalization,LTH Profile areas,Faculty of Engineering, LTH

Bruder, Gerd (redaktör/utgivare)

Olivier, Anne-Helene (redaktör/utgivare)

Cunningham, Andrew (redaktör/utgivare)

Peng, Evan Yifan (redaktör/utgivare)

Grubert, Jens (redaktör/utgivare)

Williams, Ian (redaktör/utgivare)

(creator_code:org_t)

2023
2023
Engelska 6 s.
Ingår i: Proceedings - 2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023. - 9798350328912 ; , s. 549-554

Relaterad länk:: http://dx.doi.org/10...; visa fler...; https://lup.lub.lu.s...; https://doi.org/10.1...; visa färre...

Abstract Ämnesord

Stäng

A recent singleshot multiplane image (MPI) generation enables to copy an observed reality within a camera frame into other reality domains via view synthesis. While the scene scale is unknown due to the nature of singleshot MPI processing, camera tracking algorithms can estimate depth within the application world coordinate system. Given such depth information, we propose to adjust the scale of singleshot MPI to that of the currently observed scene. We find the individual scales of the MPI layers by minimizing the differences between the depth of MPI rendering and that of camera tracking. We eventually found that many layers fall within a close depth. Therefore, we merge such layers into one to compact the MPI representation. We compared our method with baselines using real and synthetic datasets with dense and sparse depth inputs. Our results demonstrate that our algorithm achieves higher scores in image metrics and reduces MPI data amount by up to 78%.

Av författaren/redakt...: Bergfelt, Max; Larsson, Viktor; Saito, Hideo; Mori, Shohei; Bruder, Gerd; Olivier, Anne-He ...; visa fler...; Cunningham, Andr ...; Peng, Evan Yifan; Grubert, Jens; Williams, Ian; visa färre...

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