Revisiting Projective Structure from Motion: A Robust and Efficient Incremental Solution

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00328022" target="_blank" >RIV/68407700:21730/20:00328022 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TPAMI.2018.2849973" target="_blank" >https://doi.org/10.1109/TPAMI.2018.2849973</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TPAMI.2018.2849973" target="_blank" >10.1109/TPAMI.2018.2849973</a>

Result language

angličtina

Original language name

Revisiting Projective Structure from Motion: A Robust and Efficient Incremental Solution

Original language description

This paper presents a solution to the Projective Structure from Motion (PSfM) problem able to deal efficiently with missing data, outliers and, for the first time, large scale 3D reconstruction scenarios. By embedding the projective depths into the projective parameters of the points and views, we decrease the number of unknowns to estimate and improve computational speed by optimizing standard linear Least Squares systems instead of homogeneous ones. In order to do so, we show that an extension of the linear constraints from the Generalized Projective Reconstruction Theorem can be transferred to the projective parameters, ensuring also a valid projective reconstruction in the process. We use an incremental approach that, starting from a solvable sub-problem, incrementally adds views and points until completion with a robust, outliers free, procedure. To prevent error accumulation, a refinement based on alternation between new estimations of views and points is used. This can also be done with constrained non-linear optimization. Experiments with simulated data shows that our approach is performing well, both in term of the quality of the reconstruction and the capacity to handle missing data and outliers with a reduced computational time. Finally, results on real datasets shows the ability of the method to be used in medium and large scale 3D reconstruction scenarios with high ratios of missing data (up to 98%).

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20102 - Construction engineering, Municipal and structural engineering

Project

<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

e-ISSN

1939-3539

Volume of the periodical

42

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

430-443

UT code for WoS article

000508386100014

EID of the result in the Scopus database

2-s2.0-85049064425