Object-oriented design

Abstract: 1. Introduction. I. MODELING CONCEPTS. 2. Modeling as a Design Technique. 3. Object Modeling. 4. Advanced Object Modeling. 5. Dynamic Modeling. 6. Functional Modeling. II. DESIGN METHODOLOGY. 7. Methodology Preview. 8. Analysis. 9. System Design. 10. Object Design. 11. Methodology Summary. 12. Comparison of Methodologies. III. IMPLEMENTATION. 13. From Design to Implementation. 14. Programming Style. 15. Object-Oriented Languages. 16. Non-Object-Oriented Languages. 17. Databases. 18. Object Diagram Compiler. 19. Computer Animation. 20. Electrical Distribution Design System. 21. Future of Object-Oriented Technology. Appendix A: OMT Graphical Notation. Appendix B: Glossary. Index.

Abstract: Methodological guidelines for object-oriented software construction that improve the reliability of the resulting software systems are presented. It is shown that the object-oriented techniques rely on the theory of design by contract, which underlies the design of the Eiffel analysis, design, and programming language and of the supporting libraries, from which a number of examples are drawn. The theory of contract design and the role of assertions in that theory are discussed. >

Abstract: We study the problem of 3D object generation We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks and generative adversarial nets The benefits of our model are three-fold: first, the use of an adversarial criterion, instead of traditional heuristic criteria, enables the generator to capture object structure implicitly and to synthesize high-quality 3D objects; second, the generator establishes a mapping from a low-dimensional probabilistic space to the space of 3D objects, so that we can sample objects without a reference image or CAD models, and explore the 3D object manifold; third, the adversarial discriminator provides a powerful 3D shape descriptor which, learned without supervision, has wide applications in 3D object recognition Experiments demonstrate that our method generates high-quality 3D objects, and our unsupervisedly learned features achieve impressive performance on 3D object recognition, comparable with those of supervised learning methods

Abstract: This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.