3D Shape Reconstruction from Sketches via Multi-view Convolutional Networks

We propose a method for reconstructing 3D shapes from 2D sketches in the form of line drawings. Our method takes as input a single sketch, or multiple sketches, and outputs a dense point cloud representing a 3D reconstruction of the input sketch(es). The point cloud is then converted into a polygon mesh. At the heart of our method lies a deep, encoder-decoder network. The encoder converts the sketch into a compact representation encoding shape information. The decoder converts this representation into depth and normal maps capturing the underlying surface from several output viewpoints. The multi-view maps are then consolidated into a 3D point cloud by solving an optimization problem that fuses depth and normals across all viewpoints. Based on our experiments, compared to other methods, such as volumetric networks, our architecture offers several advantages, including more faithful reconstruction, higher output surface resolution, better preservation of topology and shape structure.Comment: 3DV 2017 (oral

Applying formal methods to standard development: the open distributed processing experience

Since their introduction, formal methods have been applied in various ways to different standards. This paper gives an account of these applications, focusing on one application in particular: the development of a framework for creating standards for Open Distributed Processing (ODP). Following an introduction to ODP, the paper gives an insight into the current work on formalising the architecture of the Reference Model of ODP (RM-ODP), highlighting the advantages to be gained. The different approaches currently being taken are shown, together with their associated advantages and disadvantages. The paper concludes that there is no one all-purpose approach which can be used in preference to all others, but that a combination of approaches is desirable to best fulfil the potential of formal methods in developing an architectural semantics for OD