Averkiou, M; (2015) Data-driven Modelling of Shape Structure. Doctoral thesis , UCL (University College London).

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Abstract

In recent years, the study of shape structure has shown great promise, by taking steps towards exposing shape semantics and functionality to algorithms spanning a wide range of areas in computer graphics and vision. By shape structure, we refer to the set of parts that make a shape, the relations between these parts, and the ways in which they correspond and vary between shapes of the same family. These developments have been largely driven by the abundance of 3D data, with collections of 3D models becoming increasingly prominent and websites such as Trimble 3D Warehouse offering millions of free 3D models to the public. The ability to use large amounts of data inside these shape collections for discovering shape structure has made novel approaches to acquisition, modelling, fabrication, and recognition of 3D objects possible. Discovering and modelling the structure of shapes using such data is therefore of great importance. In this thesis we address the problem of discovering and modelling shape structure from large, diverse and unorganized shape collections. Our hypothesis is that by using the large amounts of data inside such shape collections we can discover and model shape structure, and thus use such information to enable structure-aware tools for 3D modelling, including shape exploration, synthesis and editing. We make three key contributions. First, we propose an efficient algorithm for co-aligning large and diverse collections of shapes, to tackle the first challenge in detecting shape structure, which is to place shapes in a common coordinate frame. Then, we introduce a method to parameterize shapes in terms of locations and sizes of their parts, and we demonstrate its application to concurrently exploring a shape collection and synthesizing new shapes. Finally, we define a meta-representation for a shape family, which models the relations of shape parts to capture the main geometric characteristics of the family, and we demonstrate how it can be used to explore shape collections and intelligently edit shapes.

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