Radiometry propagation to large 3D point clouds from sparsely sampled ground truth

Thomas Höll, Axel Pinz

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Good radiometry of a 3D reconstruction is essential for digital conservation and versatile visualization of cultural heritage artifacts and sites. For large sites, "true" radiometry for the complete 3D point cloud is very expensive to obtain. We present a method that is capable to reconstruct the radiometric surface properties of an entire scene despite the fact that we only have access to the "true" radiometry of a small part of it. This is done in a two stage process: First, we transfer the radiometry to spatially corresponding parts of the scene, and second, we propagate these values to the entire scene using affinity information. We apply our method to 3D point clouds and 2D images, and show excellent quantitative and visually pleasing qualitative results. This approach can be of high value in many applications where users want to improve phototextured models towards high-quality yet affordable radiometry.
Originalspracheenglisch
TitelComputer Vision – ACCV 2016, Part II
Untertitel13th Asian Conference on Computer Vision
Redakteure/-innenC.S. Chen, J. Lu, KK. Ma
ErscheinungsortCham
Herausgeber (Verlag)Springer
Seiten222-235
ISBN (elektronisch)978-3-319-54190-7
ISBN (Print)978-3-319-54189-1
DOIs
PublikationsstatusVeröffentlicht - 2017
VeranstaltungAsian Conference on Computer Vision - Taipei International Convention Center, Taipei, Taiwan
Dauer: 20 Nov 201624 Nov 2016
Konferenznummer: 13

Publikationsreihe

NameLecture Notes in Computer Science
Band10117

Konferenz

KonferenzAsian Conference on Computer Vision
KurztitelACCV
LandTaiwan
OrtTaipei
Zeitraum20/11/1624/11/16

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Höll, T., & Pinz, A. (2017). Radiometry propagation to large 3D point clouds from sparsely sampled ground truth. in C. S. Chen, J. Lu, & KK. Ma (Hrsg.), Computer Vision – ACCV 2016, Part II: 13th Asian Conference on Computer Vision (S. 222-235). (Lecture Notes in Computer Science; Band 10117). Cham: Springer. https://doi.org/10.1007/978-3-319-54427-4_17