3 System Overview
existing geometry into new models, and not for creating new ge- ometry from scratch. As such, it has a synergistic relationship with other modeling systems: our tool will benefit from improvements to existing modeling systems, since there will then be larger/better databases of 3D geometry, while other modeling systems will likely benefit from including the methods described in this paper to pro- vide better utilization of existing models. Sketch modeling tools: Our system shares many ideas with 3D sketching systems, such as Sketch [Zeleznik et al. 1996] and Teddy [Igarashi et al. 1999]. Like these systems, we follow the gen- eral philosophy of keeping the user interface simple by inferring the intention of a few, easy-to-learn commands, rather than providing an exhaustive set of commands and asking the user to set several parameters for each one. However, previous systems have achieved their simplicity by limiting the complexity and types of shapes that can be created by the user. We achieve our simplicity by leveraging existing geometry stored in a database. Data-driven synthesis: Our work is largely inspired by the recent trend towards data-driven synthesis in computer graphics. The gen- eral strategy is to acquire lots of data, chop it up into parts, deter- mine which parts match, and then stitch them together in new and interesting ways [Cohen 2000]. This approach has been demon- strated recently for a number of data types, including motion cap- ture data (e.g., [Lee et al. 2002]). However, to our knowledge, it has never been applied to 3D surface modeling. Perhaps this is because 3D surfaces are more difficult to work with than other data types: they are harder to “chop up” into meaningful parts; they have more degrees of freedom affecting how they can be positioned relative to one another; they have no obvious metric for identifying similar parts in the database; and, they are harder to stitch together. These are the issues addressed in this paper. Shape interpolation: Our work shares many ideas with “shape by example” [Sloan et al. 2001] and other blending systems whose goal is to create new geometric forms from existing ones (e.g., [Lazarus and Verroust 1998]). However, our approach is quite different: we focus on recombining parts of shapes rather than mor- phing between them. We take a combinatorial approach rather than an interpolative one. Accordingly, the types of shapes that we can create and the research issues we must address are quite different. We believe that our approach is better suited for creating shapes composed of many parts, each of which has a discrete set of possi- ble forms (e.g., cars, tables, computers, etc.), while interpolation is better for generating new shapes resulting from deformations (e.g., articulated motions). Geometric search engines: Our system includes the ability to search a large database of 3D models for matches based on keyword and/or shape similarity. In this respect, it is related to 3D search engines that have recently been deployed on the Web (e.g., [Chen et al. 2003; Corney et al. 2002; Funkhouser et al. 2003; Paquet and Rioux 1997; Suzuki 2001; Vranic 2003]). Several such systems have acquired impressive databases and allow users to download 3D models for free. In our current implementation, we use the data of the Princeton 3D Model Search Engine [Min et al. 2003]. That system and ones like it employ text-based search methods similar to ours. However, their shape-based matching algorithms consider only whole-object shape matching. In this paper, we address the harder problem of part-in-whole shape matching. To our knowledge, this is the first time that a large database of example 3D models and shape-based retrieval methods have been integrated into an interactive modeling tool.
The input to our system is a database of 3D models, and the output is a new 3D model created interactively by a user. The usual cycle of operation involves choosing a model from the database, selecting a part of the model to edit, executing a search of the database for similar parts, selecting one of the models returned by the search, and then performing editing operations in which parts are cut out from the retrieved model and composited into the current model. This cycle is repeated until the user is satisfied with the resulting model and saves it to a file. The motivation for this work cycle is that it requires the user to learn very few commands (open, save, select, cut, copy, paste, undo, search, etc.), all of which are familiar to almost every computer user. A short session with our system is shown in Figure 2. Imagine that a school child wants to investigate what the Venus de Milo sculpture looked like before her arms were broken off. Although there are several theories, some believe that she was holding an apple aloft in her left hand, and her right arm was posed across her midsection [Curtis 2003]. Of course, it would be very difficult for a child to construct plausible 3D models for two arms and an apple from scratch. So, we investigate extracting those parts from other 3D models available in our database.
Figure 2: Screenshots of a ten-minute session demonstrating the main features of our system being used to investigate what Venus looked like before she lost her arms.
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