{"id":6219,"date":"2020-08-28T13:58:30","date_gmt":"2020-08-28T18:58:30","guid":{"rendered":"https:\/\/Engineering.Purdue.edu\/cdesign\/wp\/?p=6219"},"modified":"2021-08-10T14:25:14","modified_gmt":"2021-08-10T19:25:14","slug":"object-synthesis-by-learning-part-geometry-with-surface-and-volumetric-representations","status":"publish","type":"post","link":"https:\/\/engineering.purdue.edu\/cdesign\/wp\/object-synthesis-by-learning-part-geometry-with-surface-and-volumetric-representations\/","title":{"rendered":"Object Synthesis by Learning Part Geometry with Surface and Volumetric Representations"},"content":{"rendered":"

Abstract:<\/strong> We propose a conditional generative model, named Part Geometry Network (PG-Net), which synthesizes realistic objects and can be used as a robust feature descriptor for object reconstruction and classification. Surface and volumetric representations of objects have complementary properties of three-dimensional objects. Combining these modalities is more informative than using one modality alone. Therefore, PG-Net utilizes complementary properties of surface and volumetric representations by estimating curvature, surface area, and occupancy in voxel grids of objects with a single decoder as a multi-task learning. Objects are combinations of multiple parts, and therefore part geometry (PG) is essential to synthesize each part of the objects. PG-Net employs a part identifier to learn the part geometry. Additionally, we augmented a dataset by interpolating individual functional parts such as wings of an airplane, which helps learning part geometry and finding local\/global minima of PG-Net. To demonstrate the capability of learning object representations of PG-Net, we performed object reconstruction and classification tasks on two standard large-scale datasets. PG-Net outperformed the state-of-the-art methods in object synthesis, classification, and reconstruction in a large margin.<\/p>\n","protected":false},"excerpt":{"rendered":"

Abstract: We propose a conditional generative model, named Part Geometry Network (PG-Net), which synthesizes realistic objects and can be used as a robust feature descriptor for object reconstruction and classification. Surface and volumetric representations of objects have complementary properties of three-dimensional objects. Combining these modalities is more informative than using one modality alone. Therefore, PG-Net […]<\/p>\n","protected":false},"author":58,"featured_media":6230,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","_links_to":"","_links_to_target":""},"categories":[396,423,379,8,3,268,365],"tags":[412,414,402],"yoast_head":"\nObject Synthesis by Learning Part Geometry with Surface and Volumetric Representations - Convergence Design Lab, Purdue University<\/title>\n<meta name=\"description\" content=\"A conditional generative model, named Part Geometry Network, which synthesizes realistic objects and can be used as a robust feature descriptor for object reconstruction and classification.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.purdue.edu\/cdesign\/wp\/object-synthesis-by-learning-part-geometry-with-surface-and-volumetric-representations\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Object Synthesis by Learning Part Geometry with Surface and Volumetric Representations - 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