Example-based 3D object reconstruction from line drawings

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Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling (2016, background): “…in the graphics and vision community, and the relevant literature is very rich [Carlson, 1982, Tangelder and Veltkamp, 2008, Van Kaick et al., 2011, Blanz and Vetter, 1999, Kalogerakis et al., 2012, Chaudhuri et al., 2011, Xue et al., 2012, Kar et al., 2015, Bansal et al., 2016, Wu et al., 2016].”
Single Image 3D Interpreter Network (2016, methodology): “culated objects [47,57,1], like a human body or the base of a swivel chair. Typically, researchers recovered a 3D skeleton from a single image by minimizing its projection error on the 2D image plane [27,22,12,55,33,62]. Recent work in this line [1,61] demonstrated state-of-the-art performance. In c”
3D Shape Segmentation with Projective Convolutional Networks (2016, background): “Compositional part-based reasoning for 3D shapes has been shown to be effective for a large number of vision, robotics and virtual reality applications, such as cross-modal analysis of 3D shapes and color images [60, 24], skeletal tracking [42], objection detection in images [11, 30, 36], 3D object ”