Particle Swarm Optimisation Based 3D Reconstruction of Sketched Line Drawings

The purpose of this paper is to demonstrate the application of particle swarm optimisation to line drawings reconstruction. The paper’s new contribution is the application of swarm intelligence in dealing with machine perception of sketch-based modelling interfaces. Traditional descent or gradient- based optimisation algorithms are not always practical in this context because of the severe numerical noise and ill-defined objective function of the optimisation-based reconstruction problem Our results point to particle swarm optimisation as a promising alternative.This work was partially supported by Universitat Jaume I (Plan 2002 de promoció de la investigació a l’UJI, Project P1-1B2002-08, entitled “From sketch to model: new user interfaces for CAD systems”)

Three-dimensional interpretation of an imperfect line drawing.

by Leung Kin Lap.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 70-72).ACKNOWLEDGEMENTS --- p.IABSTRACT --- p.IITABLE OF CONTENTS --- p.IIITABLE OF FIGURES --- p.IVChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Contributions of the thesis --- p.2Chapter 1.2 --- Organization of the thesis --- p.4Chapter Chapter 2 --- Previous Work --- p.5Chapter 2.1 --- An overview of 3-D interpretation --- p.5Chapter 2.1.1 --- Multiple-View Clues --- p.5Chapter 2.1.2 --- Single-View Clues --- p.6Chapter 2.2 --- Line Drawing Interpretation --- p.7Chapter 2.2.1 --- Qualitative Interpretation --- p.7Chapter 2.2.2 --- Quantitative Interpretation --- p.10Chapter 2.3 --- Previous Methods of Quantitative Interpretation by Optimization --- p.12Chapter 2.3.1 --- Extremum Principle for Shape from Contour --- p.12Chapter 2.3.2 --- MSDA Algorithm --- p.14Chapter 2.4 --- Comments on Previous Work on Line Drawing Interpretation --- p.17Chapter Chapter 3 --- An Iterative Clustering Procedure for Imperfect Line Drawings --- p.18Chapter 3.1 --- Shape Constraints --- p.19Chapter 3.2 --- Problem Formulation --- p.20Chapter 3.3 --- Solution Steps --- p.25Chapter 3.4 --- Nearest-Neighbor Clustering Algorithm --- p.37Chapter 3.5 --- Discussion --- p.38Chapter Chapter 4 --- Experimental Results --- p.40Chapter 4.1 --- Synthetic Line Drawings --- p.40Chapter 4.2 --- Real Line Drawing --- p.42Chapter 4.2.1 --- Recovery of real images --- p.42Chapter Chapter 5 --- Conclusion and Future Work --- p.65Appendix A --- p.67Chapter A. 1 --- Gradient Space Concept --- p.67Chapter A. 2 --- Shading of images --- p.69Appendix B --- p.7

3D Object Perception Using Gradient Descent

A new algorithm is presented for interpreting two-dimensional (2D) line drawings as threedimensional (3D) objects without models. Even though no explicit models or additional heuristics are included, the algorithm tends to reach the same 3D interpretations of 2D line drawings that humans do. The algorithm explicitly calculates the partial derivatives of Marill's Minimum Standard Deviation of Angles (MSDA) with respect to all adjustable parameters, and follows this gradient to minimize SDA. For an image with lines meeting at m points forming n angles, the gradient descent algorithm requires O(n) time to adjust all the points, while Marill’s method required O(mn) time to do so. Experimental results on various line drawing objects show that this gradient descent algorithm running on a Macintosh II is one to two orders of magnitude faster than the MSDA algorithm running on a Symbolics, while still giving comparable results