000256753 001__ 256753
000256753 005__ 20190812210029.0
000256753 020__ $$a978-1-5106-2076-6
000256753 022__ $$a0277-786X
000256753 022__ $$a1996-756X
000256753 02470 $$a000450861700016$$2isi
000256753 0247_ $$a10.1117/12.2321518$$2doi
000256753 037__ $$aCONF
000256753 245__ $$aPoint cloud subjective evaluation methodology based on reconstructed surfaces
000256753 269__ $$a2018
000256753 260__ $$c2018$$bSPIE-INT SOC OPTICAL ENGINEERING$$aBellingham
000256753 300__ $$a14
000256753 336__ $$aConference Papers
000256753 520__ $$aPoint clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation transmission and storing. However, the selection of appropriate compression schemes requires effective quality evaluation. In this work a subjective quality evaluation of point clouds using a surface representation is analyzed. Using a set of point cloud data objects encoded with the popular octree pruning method with different qualities, a subjective evaluation was designed. The point cloud geometry was presented to observers in the form of a movie showing the 3D Poisson reconstructed surface without textural information with the point of view changing in time. Subjective evaluations were performed in three different laboratories. Scores obtained from each test were correlated and no statistical differences were observed. Scores were also correlated with previous subjective tests and a good correlation was obtained when compared with mesh rendering in 2D monitors. Moreover, the results were correlated with state of the art point cloud objective metrics revealing poor correlation. Likewise, the correlation with a subjective test using a different representation of the point cloud data also showed poor correlation. These results suggest the need for more reliable objective quality metrics and further studies on adequate point cloud data representations.
000256753 6531_ $$asubjective quality assessment
000256753 6531_ $$apoint cloud
000256753 6531_ $$aquality metrics
000256753 700__ $$g274471$$aAlexiou, Evangelos$$0250078
000256753 700__ $$aPinheiro, Antonio
000256753 700__ $$aDuarte, Carlos
000256753 700__ $$aMatkovic, Dragan
000256753 700__ $$aDumic, Emil
000256753 700__ $$ada Silva Cruz, Luis A.
000256753 700__ $$aDmitrovic, Lovorka Gotal
000256753 700__ $$aBernardo, Marco V.
000256753 700__ $$aPereira, Manuela
000256753 700__ $$0240223$$aEbrahimi, Touradj$$g105043
000256753 711__ $$cSan Diego, CA, USA$$dAugust 20-23, 2018$$aConference on Applications of Digital Image Processing XLI
000256753 7112_ $$dAugust 19-23, 2018$$cSan Diego, California, USA$$aSPIE Optical Engineering + Applications
000256753 773__ $$q107520H$$j10752$$tApplications of Digital Image Processing XLI
000256753 8564_ $$uhttps://infoscience.epfl.ch/record/256753/files/SPIE.pdf$$s1965668
000256753 8560_ $$fevangelos.alexiou@epfl.ch
000256753 909C0 $$xUS02700$$pMMSPL$$mtouradj.ebrahimi@epfl.ch$$0252077$$xU11889
000256753 909CO $$qGLOBAL_SET$$pconf$$pSTI$$ooai:infoscience.epfl.ch:256753
000256753 960__ $$aevangelos.alexiou@epfl.ch
000256753 961__ $$apierre.devaud@epfl.ch
000256753 973__ $$aEPFL$$rREVIEWED
000256753 980__ $$aCONF
000256753 981__ $$aoverwrite