Abstract:Structure from Motion (SfM) can produce highly detailed 3D reconstructions, but distinguishing real surface roughness from reconstruction noise and geometric inaccuracies has always been a difficult problem to solve. Existing SfM commercial solutions achieve noise removal by a combination of aggressive global smoothing and the reconstructed texture for smaller details, which is a subpar solution when the results are used for surface inspection. Other noise estimation and removal algorithms do not take advantag… Show more
“…Metrics were introduced and considered to present qualitative and quantitative assessments of the results. Finally, Nikolov and Madsen [ 5 ] introduced a metric for noise estimation in highly detailed 3D SfM reconstructions. In particular, the authors discussed a possible approach to distinguishing real surface roughness from reconstruction noise and proposed a number of geometrical and statistical metrics for noise assessment based on both the reconstructed object and the capturing camera setup.…”
The recent development and rapid evolution of modern sensors and new processing strategies of collected data have paved the way for innovations in photogrammetry and remote sensing [...]
“…Metrics were introduced and considered to present qualitative and quantitative assessments of the results. Finally, Nikolov and Madsen [ 5 ] introduced a metric for noise estimation in highly detailed 3D SfM reconstructions. In particular, the authors discussed a possible approach to distinguishing real surface roughness from reconstruction noise and proposed a number of geometrical and statistical metrics for noise assessment based on both the reconstructed object and the capturing camera setup.…”
The recent development and rapid evolution of modern sensors and new processing strategies of collected data have paved the way for innovations in photogrammetry and remote sensing [...]
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