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

Thomas Höll, Axel Pinz

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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.
Original languageEnglish
Title of host publicationComputer Vision – ACCV 2016, Part II
Subtitle of host publication13th Asian Conference on Computer Vision
EditorsC.S. Chen, J. Lu, KK. Ma
Place of PublicationCham
PublisherSpringer
Pages222-235
ISBN (Electronic)978-3-319-54190-7
ISBN (Print)978-3-319-54189-1
DOIs
Publication statusPublished - 2017
EventAsian Conference on Computer Vision - Taipei International Convention Center, Taipei, Taiwan, Province of China
Duration: 20 Nov 201624 Nov 2016
Conference number: 13

Publication series

NameLecture Notes in Computer Science
Volume10117

Conference

ConferenceAsian Conference on Computer Vision
Abbreviated titleACCV
CountryTaiwan, Province of China
CityTaipei
Period20/11/1624/11/16

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cultural heritage
visualization
artifact
method

Cite this

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 (Eds.), Computer Vision – ACCV 2016, Part II: 13th Asian Conference on Computer Vision (pp. 222-235). (Lecture Notes in Computer Science; Vol. 10117). Cham: Springer. https://doi.org/10.1007/978-3-319-54427-4_17

Radiometry propagation to large 3D point clouds from sparsely sampled ground truth. / Höll, Thomas; Pinz, Axel.

Computer Vision – ACCV 2016, Part II: 13th Asian Conference on Computer Vision. ed. / C.S. Chen; J. Lu; KK. Ma. Cham : Springer, 2017. p. 222-235 (Lecture Notes in Computer Science; Vol. 10117).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Höll, T & Pinz, A 2017, Radiometry propagation to large 3D point clouds from sparsely sampled ground truth. in CS Chen, J Lu & KK Ma (eds), Computer Vision – ACCV 2016, Part II: 13th Asian Conference on Computer Vision. Lecture Notes in Computer Science, vol. 10117, Springer, Cham, pp. 222-235, Asian Conference on Computer Vision, Taipei, Taiwan, Province of China, 20/11/16. https://doi.org/10.1007/978-3-319-54427-4_17
Höll T, Pinz A. Radiometry propagation to large 3D point clouds from sparsely sampled ground truth. In Chen CS, Lu J, Ma KK, editors, Computer Vision – ACCV 2016, Part II: 13th Asian Conference on Computer Vision. Cham: Springer. 2017. p. 222-235. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-54427-4_17
Höll, Thomas ; Pinz, Axel. / Radiometry propagation to large 3D point clouds from sparsely sampled ground truth. Computer Vision – ACCV 2016, Part II: 13th Asian Conference on Computer Vision. editor / C.S. Chen ; J. Lu ; KK. Ma. Cham : Springer, 2017. pp. 222-235 (Lecture Notes in Computer Science).
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