3D Functionality Analysis for Shape Modeling via Partial Matching

Abstract

3D Shape modeling is an important research area in computer graphics. Making sure that the modeled shapes are functional can largely facilitate the modeling process, since then the user is able to create more realistic shapes. To ensure that the generated shapes can have complex functionalities, an important requirement for the modeling system is to enable cross-category modeling (or shape hybridization), i.e., combining shapes from different categories to create shapes with multiple functionalities. However, without a proper method for evaluating the functionality of hybrid shapes, traditional shape modeling methods are not functionality-aware, and often produce shapes that are not functionally plausible.In this thesis, we present an analysis method for evaluating the functionality of 3D shapes, especially hybrid shapes with multiple functionalities. Our method is based on functionality partial matching, which localizes the functionality analysis down to the partial shape level. We show that functionality partial matching enables functionality analysis for hybrid shapes.Moreover, we incorporate functionality partial matching into an evolutionary shape modeling framework, which evolves an initial set of shapes through crossover operations at the level of shape parts, making the evolutionary process functionalityaware. We show that our functionality-aware model evolution can produce a large and diverse population of functionally plausible hybrid shapes. We also show that the generated hybrid shapes can be used to augment existing 3D shape datasets to train data-driven machine learning methods for shape segmentation.