This paper reports the results of the SHREC 2015 track on retrieval of non-rigid (textured) shapes from low quality3D models. This track has been organized to test the ability of the algorithms recently proposed by researchersfor the retrieval of articulated and textured shapes to deal with real-world deformations and acquisition noise.For this reason we acquired with low cost devices models of plush toys lying on different sides on a platform,with articulated deformations and with different illumination conditions. We obtained in this way three novel andchallenging datasets that have been used to organize a contest where the proposed task was the retrieval of istancesof the same toy within acquired shapes collections, given a query model. The differences in datasets and tasks wererelated to the fact that one dataset was built without applying texture to shapes, and the others had texture appliedto vertices with two different methods. We evaluated the retrieval results of the proposed techniques using standardevaluation measures: Precision-Recall curve; E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2), Mean Average Precision. Robustness of methods against texture and shapedeformation has also been separately evaluated.

SHREC'15 Track: Retrieval of Non-rigid (textured) Shapes Using Low Quality 3D Models

GIACHETTI, Andrea;
2015-01-01

Abstract

This paper reports the results of the SHREC 2015 track on retrieval of non-rigid (textured) shapes from low quality3D models. This track has been organized to test the ability of the algorithms recently proposed by researchersfor the retrieval of articulated and textured shapes to deal with real-world deformations and acquisition noise.For this reason we acquired with low cost devices models of plush toys lying on different sides on a platform,with articulated deformations and with different illumination conditions. We obtained in this way three novel andchallenging datasets that have been used to organize a contest where the proposed task was the retrieval of istancesof the same toy within acquired shapes collections, given a query model. The differences in datasets and tasks wererelated to the fact that one dataset was built without applying texture to shapes, and the others had texture appliedto vertices with two different methods. We evaluated the retrieval results of the proposed techniques using standardevaluation measures: Precision-Recall curve; E-Measure; Discounted Cumulative Gain; Nearest Neighbor, First-Tier (Tier1) and Second-Tier (Tier2), Mean Average Precision. Robustness of methods against texture and shapedeformation has also been separately evaluated.
2015
9783905674781
Shape retrieval; nonrigid; texture; Depth sensor
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/917183
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