Multi-image 3D reconstruction data evaluation

By Anestis Koutsoudis, B. Vidmar, G. Ioannakis, Fotis Arnaoutoglou, G. Pavlidis, C. Chamzas. 2014

332 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geology, Instrumentation

View Paper: https://www.semanticscholar.org/paper/cf97752eb7a0c262b9e24051eab24609680476f7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Maria Grazia Spera
2. Mark W. Smith
3. Erica Nocerino
4. Fabio Remondino
5. Francesco Nex
6. Fabio Menna
7. Duncan Quincey
8. Jonathan L. Carrivick

In Geology

1. Mark W. Smith
2. Fabio Remondino
3. Francesco Nex
4. Duncan Quincey
5. Jonathan L. Carrivick
6. Ismael Colomina
7. Pere Molina

In Instrumentation

1. Fabio Remondino

Citations in the corpus

• Structure from motion photogrammetry in physical geography (2016, methodology): “In related fields, SfM-MVS is used regularly; for example SfM-MVS is now used in archaeology (De Reu et al., 2014; McCarthy, 2014) and as part of efforts to preserve cultural heritage digitally (Gallo et al., 2014; Koutsoudis et al., 2014).”
• State of the art in high density image matching (2014, background): “Several recent publications have compared ranging and imaging techniques based on factors such as accuracy, resolution and dense 3D reconstructions (Kersten and Lindstaedt, 2012; Opitz et al., 2012; Koutsoudis et al., 2014).”