SHREC 2020 Track: 6D Object Pose Estimation

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dc.contributor.authorYuan, Honglinen_US
dc.contributor.authorVeltkamp, Remco C.en_US
dc.contributor.authorAlbanis, Georgiosen_US
dc.contributor.authorZioulis, Nikolaosen_US
dc.contributor.authorZarpalas, Dimitriosen_US
dc.contributor.authorDaras, Petrosen_US
dc.contributor.editorSchreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.en_US
dc.date.accessioned2020-09-03T09:50:28Z
dc.date.available2020-09-03T09:50:28Z
dc.date.issued2020
dc.description.abstract6D 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.en_US
dc.description.sectionheadersSHREC Short Papers
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20201164
dc.identifier.isbn978-3-03868-126-7
dc.identifier.issn1997-0471
dc.identifier.pages45-52
dc.identifier.urihttps://doi.org/10.2312/3dor.20201164
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20201164
dc.publisherThe Eurographics Associationen_US
dc.subjectInformation systems
dc.subjectMultimedia and multimodal retrieval
dc.subjectEvaluation of retrieval results
dc.subjectSpecialized information retrieval
dc.titleSHREC 2020 Track: 6D Object Pose Estimationen_US
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3DOR 2020 - Short Papers