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we present a method to learn a joint embedding of 3d scan objects and CAD models scanned objects and handcrafted CAD models both contain a rich amount of information for shape and scene understanding geometrically low they have strong lower level differences while scan objects can be noisy incomplete and cluttered cat models are mostly compact clean and complete these differences make it difficult to map objects from both domains to the same space where semantically similar objects like close together to this end we propose a novel 3d CNN based approach designed as a stacked hourglass the first hourglass takes as input the region surrounding a scan object as a binary occupancy grid it is composed of an encoder which leads to two decoders which segment the object geometry from background clutter the second hourglass encodes the segmented but partial geometry and learns to complete it finally we have an encoder which embeds the complete object together with matching and a non matching CA
