Browsing by Author "Li, Bo"

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    Extended 2D Scene Image-Based 3D Scene Retrieval
    (The Eurographics Association, 2019) Abdul-Rashid, Hameed; Yuan, Juefei; Li, Bo; Lu, Yijuan; Schreck, Tobias; Bui, Ngoc-Minh; Do, Trong-Le; Holenderski, Mike; Jarnikov, Dmitri; Le, Khiem T.; Menkovski, Vlado; Nguyen, Khac-Tuan; Nguyen, Thanh-An; Nguyen, Vinh-Tiep; Ninh, Tu V.; Rey, Perez; Tran, Minh-Triet; Wang, Tianyang; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    In the months following our SHREC 2018 - 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 30 classes, resulting in a new benchmark SceneIBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and new 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, the United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performance metrics, offers an indepth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied to this exigent retrieval task. To further enrich the current state of 3D scene understanding and retrieval, our evaluation toolkit, all participating methods' results and the comprehensive 2D/3D benchmark have all been made publicly available.
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    Extended 2D Scene Sketch-Based 3D Scene Retrieval
    (The Eurographics Association, 2019) Yuan, Juefei; Abdul-Rashid, Hameed; Li, Bo; Lu, Yijuan; Schreck, Tobias; Bui, Ngoc-Minh; Do, Trong-Le; Nguyen, Khac-Tuan; Nguyen, Thanh-An; Nguyen, Vinh-Tiep; Tran, Minh-Triet; Wang, Tianyang; Biasotti, Silvia and Lavoué, Guillaume and Veltkamp, Remco
    Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications.