3DOR 18

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https://diglib.eg.org/handle/10.2312/2632165

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Browsing 3DOR 18 by Subject "Information Search and Retrieval"

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    2D Image-Based 3D Scene Retrieval
    (The Eurographics Association, 2018) Abdul-Rashid, Hameed; Yuan, Juefei; Li, Bo; Lu, Yijuan; Bai, Song; Bai, Xiang; Bui, Ngoc-Minh; Do, Minh N.; Do, Trong-Le; Duong, Anh-Duc; He, Xinwei; Le, Tu-Khiem; Li, Wenhui; Liu, Anan; Liu, Xiaolong; Nguyen, Khac-Tuan; Nguyen, Vinh-Tiep; Nie, Weizhi; Ninh, Van-Tu; Su, Yuting; Ton-That, Vinh; Tran, Minh-Triet; Xiang, Shu; Zhou, Heyu; Zhou, Yang; Zhou, Zhichao; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    2D scene image-based 3D scene retrieval is a new research topic in the field of 3D object retrieval. Given a 2D scene image, it is to search for relevant 3D scenes from a dataset. It has an intuitive and convenient framework which allows users to learn, search, and utilize the retrieved results for vast related applications, such as automatic 3D content generation for 3D movie, game and animation production, robotic vision, and consumer electronics apps development, and autonomous vehicles. To advance this promising research, we organize this SHREC track and build the first 2D scene image-based 3D scene retrieval benchmark by collecting 2D images from ImageNet and 3D scenes from Google 3D Warehouse. The benchmark contains uniformly classified 10,000 2D scene images and 1,000 3D scene models of ten (10) categories. In this track, seven (7) groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only three (3) groups have successfully submitted ten (10) runs of five methods. To have a comprehensive comparison, seven (7) commonly-used retrieval performance metrics have been used to evaluate their retrieval performance. We also suggest several future research directions for this research topic. We wish this publicly available [ARYLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and boost the research of 2D scene image-based 3D scene retrieval and its applications.
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    2D Scene Sketch-Based 3D Scene Retrieval
    (The Eurographics Association, 2018) Yuan, Juefei; Li, Bo; Lu, Yijuan; Bai, Song; Bai, Xiang; Bui, Ngoc-Minh; Do, Minh N.; Do, Trong-Le; Duong, Anh-Duc; He, Xinwei; Le, Tu-Khiem; Li, Wenhui; Liu, Anan; Liu, Xiaolong; Nguyen, Khac-Tuan; Nguyen, Vinh-Tiep; Nie, Weizhi; Ninh, Van-Tu; Su, Yuting; Ton-That, Vinh; Tran, Minh-Triet; Xiang, Shu; Zhou, Heyu; Zhou, Yang; Zhou, Zhichao; Telea, Alex and Theoharis, Theoharis and Veltkamp, Remco
    Sketch-based 3D model retrieval has the intuitiveness advantage over other types of retrieval schemes. Currently, there is a lot of research in sketch-based 3D model retrieval, which usually targets the problem of retrieving a list of candidate 3D models using a single sketch as input. 2D scene sketch-based 3D scene retrieval is a brand new research topic in the field of 3D object retrieval. Unlike traditional sketch-based 3D model retrieval which ideally assumes that a query sketch contains only a single object, this is a new 3D model retrieval topic within the context of a 2D scene sketch which contains several objects that may overlap with each other and thus be occluded and also have relative location configurations. It is challenging due to the semantic gap existing between the iconic 2D representation of sketches and more accurate 3D representation of 3D models. But it also has vast applications such as 3D scene reconstruction, autonomous driving cars, 3D geometry video retrieval, and 3D AR/VR Entertainment. Therefore, this research topic deserves our further exploration. To promote this interesting research, we organize this SHREC track and build the first 2D scene sketch-based 3D scene retrieval benchmark by collecting 3D scenes from Google 3D Warehouse and utilizing our previously proposed 2D scene sketch dataset Scene250. The objective of this track is to evaluate the performance of different 2D scene sketch-based 3D scene retrieval algorithms using a 2D sketch query dataset and a 3D Warehouse model dataset. The benchmark contains 250 scene sketches and 1000 3D scene models, and both are equally classified into 10 classes. In this track, six groups from five countries (China, Chile, USA, UK, and Vietnam) have registered for the track, while due to many challenges involved, only 3 groups have successfully submitted 8 runs. The retrieval performance of submitted results has been evaluated using 7 commonly used retrieval performance metrics. We also conduct a thorough analysis and discussion on those methods, and suggest several future research directions to tackle this research problem. We wish this publicly available [YLL18] benchmark, comparative evaluation results and corresponding evaluation code, will further enrich and advance the research of 2D scene sketch-based 3D scene retrieval and its applications.