1,721,055 research outputs found
Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter
No full textEurographics Workshop on 3D Object Retrieva
SHREC 2020 Track: 6D Object Pose Estimation
No full text6D pose estimation is crucial for augmented reality, virtual reality, robotic manipulation and visual navigation. However, the problem is challenging due to the variety of objects in the real world. They have varying 3D shape and their appearances in captured images are affected by sensor noise, changing lighting conditions and occlusions between objects. Different pose estimation methods have different strengths and weaknesses, depending on feature representations and scene contents. At the same time, existing 3D datasets that are used for data-driven methods to estimate 6D poses have limited view angles and low resolution. To address these issues, we organize the Shape Retrieval Challenge benchmark on 6D pose estimation and create a physically accurate simulator that is able to generate photo-realistic color-and-depth image pairs with corresponding ground truth 6D poses. From captured color and depth images, we use this simulator to generate a 3D dataset which has 400 photo-realistic synthesized color-and-depth image pairs with various view angles for training, and another 100 captured and synthetic images for testing. Five research groups register in this track and two of them submitted their results. Data-driven methods are the current trend in 6D object pose estimation and our evaluation results show that approaches which fully exploit the color and geometric features are more robust for 6D pose estimation of reflective and texture-less objects and occlusion. This benchmark and comparative evaluation results have the potential to further enrich and boost the research of 6D object pose estimation and its applications.Eurographics Workshop on 3D Object RetrievalSHREC Short Paper
Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter
No full textEurographics Workshop on 3D Object Retrieva
Eurographics Workshop on 3D Object Retrieval - Short Papers: Frontmatter
No full textEurographics Workshop on 3D Object Retrieva
Design Considerations for Building a Scalable Digital Version of a Multi-player Educational Board Game for a MOOC in Logistics and Transportation
No full textWith more flexible and large-scale learning environments, new design requirements for games emerge. Massive Open Online Courses (MOOCs) are one of the most important innovations in the learning field. Still, it is a challenge to motivate learners and to keep them motivated in such huge learning environments. To address this challenge, we redesigned a board game targeting at an integrated view on disruption and communication management in an intermodal transportation situation. From the redesign, we have learned that an online game works better with fewer roles, requires immediate feedback, and an engaging way of challenge to keep players motivated. Our findings can inform the design of games for large groups of players in an online environment.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Policy AnalysisOrganisation & Governanc
SHREC 2020 track: 6D object pose estimation
Full text link6D pose estimation is crucial for augmented reality, virtual reality, robotic manipulation and visual navigation. However, the problem is challenging due to the variety of objects in the real world. They have varying 3D shape and their appearances in captured images are affected by sensor noise, changing lighting conditions and occlusions between objects. Different pose estimation methods have different strengths and weaknesses, depending on feature representations and scene contents. At the same time, existing 3D datasets that are used for data-driven methods to estimate 6D poses have limited view angles and low resolution. To address these issues, we organize the Shape Retrieval Challenge benchmark on 6D pose estimation and create a physically accurate simulator that is able to generate photo-realistic color-and-depth image pairs with corresponding ground truth 6D poses. From captured color and depth images, we use this simulator to generate a 3D dataset which has 400 photo-realistic synthesized color-and-depth image pairs with various view angles for training, and another 100 captured and synthetic images for testing. Five research groups register in this track and two of them submitted their results. Data-driven methods are the current trend in 6D object pose estimation and our evaluation results show that approaches which fully exploit the color and geometric features are more robust for 6D pose estimation of reflective and texture-less objects and occlusion. This benchmark and comparative evaluation results have the potential to further enrich and boost the research of 6D object pose estimation and its applications
Orientation invariant 3D object classification using hough transform based methods
No full textIn comparison to the 2D case, object class recognition in 3D is a much less researched area. However, with the advent of affordable 3D acquisition technology and the growing popularity of 3D content, its relevance is steadily increasing. Just as in the 2D case, 3D data is often partial, noisy and without prior segmentation. Moreover, the object is rarely observed in a canonical frame of reference with respect to orientation (or scale). We propose a method, using Hough-voting for local 3D features, which is orientation (and scale) invariant.status: Publishe
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