3D Reconstruction
Structural analysis of minuscule height objects holds paramount significance in fields such as industrial manufacturing and medical testing. Currently, 3D reconstruction method based on shape from focus (SFF) has emerged as an efficacious approach for acquiring submicron-level height change information. A novel multi-field SFF(MF-SFF) framework incorporates pulse-controlled continuous acquisition methods and parallel chain processing (PCP) strategy, effectively addressing challenges associated with minuscule height objects.
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Includes the performance and scaling result of MemXCT.
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We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c
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The Contest: Goals and Organisation
The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.
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