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3D models of objects and scenes are critical to many academic disciplines and industrial applications. Of particular interest is the emerging opportunity for 3D graphics to serve artificial intelligence: computer vision systems can benefit from synthetically‐generated training data rendered from virtual 3D scenes, and robots can be trained to navigate in and interact with real‐world environments by first acquiring skills in simulated ones. One of the most promising ways to achieve this is by learning and applying generative models of 3D content: computer programs that can synthesize new 3D shapes and scenes. To allow users to edit and manipulate the synthesized 3D content to achieve their goals, the generative model should also be structure‐aware: it should express 3D shapes and scenes using abstractions that allow manipulation of their high‐level structure. This state‐of‐the‐art report surveys historical work and recent progress on learning structure‐aware generative models of 3D shapes and scenes. We present fundamental representations of 3D shape and scene geometry and structures, describe prominent methodologies including probabilistic models, deep generative models, program synthesis, and neural networks for structured data, and cover many recent methods for structure‐aware synthesis of 3D shapes and indoor scenes. [ABSTRACT FROM AUTHOR]

Several applications in shape modeling and exploration require identification and extraction of a 3D shape part matching a 2D sketch. We present CustomCut, an on-demand part extraction algorithm. Given a sketched query, CustomCut automatically retrieves partially matching shapes from a database, identifies the region optimally matching the query in each shape, and extracts this region to produce a customized part that can be used in various modeling applications. In contrast to earlier work on sketch-based retrieval of predefined parts, our approach can extract arbitrary parts from input shapes and does not rely on a prior segmentation into semantic components. The method is based on a novel data structure for fast retrieval of partial matches: the randomized compound k-NN graph built on multi-view shape projections. We also employ a coarse-to-fine strategy to progressively refine part boundaries down to the level of individual faces. Experimental results indicate that our approach provides an intuitive and easy means to extract customized parts from a shape database, and significantly expands the design space for the user. We demonstrate several applications of our method to shape design and exploration. [ABSTRACT FROM AUTHOR]

The crystal structure of the title compound, C32H24O4, contains three fused di­hydro­pyran rings ( A, B and C); ring A is fused with a benzene ring while the other two rings, B and C, are fused with naphthalene rings. Ring A adopts a half-chair conformation with an equatorial methoxy group, whereas ring B assumes a distorted half-chair conformation, the A/ B ring junction being trans. Ring C adopts a distorted half-boat conformation and is nearly orthogonal to ring B. Ring C is inclined to the best plane of ring A at an angle of 112.1 (1)°. [ABSTRACT FROM AUTHOR]

In the title compound, [Co(NCS)2(C6H6N2O)2(H2O)2], the Co(II) cation is located on an inversion centre and is coordinated by two thio-cyanate anions, two nicotinamide mol-ecules and two water mol-ecules in a distorted N2O2S2 octa-hedral geometry. The amide group is twisted by 31.30 (16)° with respect to the pyridine ring. In the crystal, mol-ecules are linked by O-H⋯O, O-H⋯S and N-H⋯S hydrogen bonds into a three-dimensional supra-molecular network. Weak π-π stacking is observed between parallel pyridine rings of adjacent mol-ecules, the centroid-centroid distance being 3.8270 (19) Å.