Organizing and using 3D data related to physical sites is important in many applications such as historical reconstruction, architectural design, and urban planning. However, no method has been developed that exploits the full range of data types available for such sites. Useful data often comes from historical sources, and requires substantial processing to be useful. Some of this processing can be automated, but some of it must be done by humans. An as-yet unsolved problem is how to coordinate human effort to efficiently carry out this process. In the current project the PIs will address quantitative and qualitative accuracy issues in reconstructing 3D sites so as to allow for input and participation by different populations in building data sets, and will demonstrate a variety of applications using a heterogeneous 3D site representation. Specifically, the work will make the following contributions: new techniques for annotating heterogeneous input will be developed, balancing automated and human input; new techniques for coordinating digital computation, human computation, and machine learning will be devised; new tools for architectural analysis and design, and for material weathering analysis, will be developed based on the new 3D representation; and new ideas for storytelling from 3D data will be demonstrated. Project outcomes will include a new organization of heterogeneous data for 3D sites, new insights into the relative contributions of automated techniques and human computation in the domain of 3D site data (which will be applicable to other challenging problems involving large complex data sets), new algorithms for reconstructing 3D models, and new techniques for conducting studies in architecture and in cultural heritage.
Broader Impacts: This research will have a strong impact on architectural-design and cultural heritage documentation, interpretation and communication. The various phases of the project will involve students at both the graduate and undergraduate levels, and in diverse disciplines including computer science, architecture, and art history. The PIs will produce teaching modules based on this work targeted at computer science, architecture, and cultural heritage.
