3DOR 16

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https://diglib.eg.org/handle/10.2312/15070

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Browsing 3DOR 16 by Subject "H.3.3 [Computer Graphics]"

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    3D Sketch-Based 3D Shape Retrieval
    (The Eurographics Association, 2016) Li, Bo; Lu, Yijuan; Duan, Fuqing; Dong, Shuilong; Fan, Yachun; Qian, Lu; Laga, Hamid; Li, Haisheng; Li, Yuxiang; Liu, Peng; Ovsjanikov, Maks; Tabia, Hedi; Ye, Yuxiang; Yin, Huanpu; Xue, Ziyu; A. Ferreira and A. Giachetti and D. Giorgi
    Sketch-based 3D shape retrieval has unique representation availability of the queries and vast applications. Therefore, it has received more and more attentions in the research community of content-based 3D object retrieval. However, sketch-based 3D shape retrieval is a challenging research topic due to the semantic gap existing between the inaccurate representation of sketches and accurate representation of 3D models. In order to enrich and advance the study of sketch-based 3D shape retrieval, we initialize the research on 3D sketch-based 3D model retrieval and collect a 3D sketch dataset based on a developed 3D sketching interface which facilitates us to draw 3D sketches in the air while standing in front of a Microsoft Kinect. The objective of this track is to evaluate the performance of different 3D sketch-based 3D model retrieval algorithms using the hand-drawn 3D sketch query dataset and a generic 3D model target dataset. The benchmark contains 300 sketches that are evenly divided into 30 classes, as well as 1258 3D models that are classified into 90 classes. In this track, nine runs have been submitted by five groups and their retrieval performance has been evaluated using seven commonly used retrieval performance metrics.We wish this benchmark, the comparative evaluation results and the corresponding evaluation code will further promote sketch-based 3D shape retrieval and its applications.
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    Large-Scale 3D Shape Retrieval from ShapeNet Core55
    (The Eurographics Association, 2016) Savva, M.; Yu, F.; Su, Hao; Aono, M.; Chen, B.; Cohen-Or, D.; Deng, W.; Su, Hang; Bai, S.; Bai, X.; Fish, N.; Han, J.; Kalogerakis, E.; Learned-Miller, E. G.; Li, Y.; Liao, M.; Maji, S.; Tatsuma, A.; Wang, Y.; Zhang, N.; Zhou, Z.; A. Ferreira and A. Giachetti and D. Giorgi
    With the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track aims to provide a benchmark to evaluate large-scale shape retrieval based on the ShapeNet dataset. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Five participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. We find the submitted methods work reasonably well on the track benchmark, but we also see significant space for improvement by future algorithms. We release all the data, results, and evaluation code for the benefit of the community.