Pregled bibliografske jedinice broj: 717158
Recognizing 3D Objects from a Limited Number of Views using Temporal Ensembles of Shape Functions
Recognizing 3D Objects from a Limited Number of Views using Temporal Ensembles of Shape Functions // CCVW 2014 Proceedings of the Croatian Computer Vision Workshop / Lončarić, Sven ; Subašić, Marko (ur.).
Zagreb: Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu, 2014. str. 44-49 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Recognizing 3D Objects from a Limited Number of Views using Temporal Ensembles of Shape Functions
Autori
Brkić, Karla ; Šegvić, Siniša ; Kalafatić, Zoran ; Aldoma, Aitor ; Vincze, Markus
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
CCVW 2014 Proceedings of the Croatian Computer Vision Workshop
/ Lončarić, Sven ; Subašić, Marko - Zagreb : Fakultet elektrotehnike i računarstva Sveučilišta u Zagrebu, 2014, 44-49
Skup
3rd Croatian Computer Vision Workshop
Mjesto i datum
Zagreb, Hrvatska, 16.09.2014
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
3D object recognition; shape functions; temporal ensembles of shape functions; object descriptors; RGB-D data; ESF; TESF
Sažetak
We consider the problem of 3D object recognition, assuming an application scenario involving a mobile robot equipped with an RGB-D camera. In order to simulate this scenario, we use a database of 3D objects and render partial point clouds representing depth views of an object. Using the rendered point clouds, we represent each object with an object descriptor called temporal ensemble of shape functions (TESF). We investigate leave-one-out 1-NN classification performance on the considered dataset depending on the number of views used to build TESF descriptors, as well as the possibility of matching the descriptors built using varying numbers of views. We establish the baseline by classifying individual view ESF descriptors. Our experiments suggest that classifying TESF descriptors outperforms individual ESF classification, and that TESF descriptors offer reasonable descriptivity even when very few views are used. The performance remains very good even if the query TESF and the nearest TESF are built using a differing number of views.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb