Structure-Preserving Retargeting of Irregular 3D Architecture

Jinjie Lin, Daniel Cohen-Or, Hao (Richard) Zhang, Cheng Liang, Andrei Sharf, Oliver Deussen, Baoquan Chen

ACM SIGGRAPH Asia 2011

Structure-Preserving Retargeting of Irregular 3D Architecture
Abstract:
We present an algorithm for interactive structure-preserving retargeting of irregular 3D architecture models, offering the modeler an easy-to-use tool to quickly generate a variety of 3D models that resemble an input piece in its structural style. Working on a more global and structural level of the input, our technique allows and even encourages replication of its structural elements, while taking into account of their semantics and expected geometric interrelations such as alignments and adjacency. The algorithm performs automatic replication and scaling of these elements while preserving their structures. Instead of solving a complex constrained optimization, we decompose the input model into a set of sequences, each of which is a 1D structure that is relatively straightforward to retarget. As the sequences are retargeted in turn, they progressively constrain the retargeting of the remaining sequences. We demonstrate interactivity and variability of results from our retargeting algorithm using many examples modeled after real-world architectures exhibiting various forms of irregularity.
Paper: PDF [27.6MB] [1.71MB]
Slides: PPTX [73MB]
Video: MOV [90.7MB]
Program: click here
Results:

Figure 2: Major components of our retargeting algorithm. User interactively defines (b) the box hierarchy and behavior attributes (red: replicated; green: scaled; blue: fixed). The rest of the algorithm is automatic. An ordered set of retargetable sequences is computed at each level of the hierarchy --- two are shown here in (c). Retargeting is executed by a traversal of the box hierarchy and operating on the retargetable sequences in turn --- from left to right, three such sequences are shown with red border in (c).



Figure 4. Different retargeting results by changing the behavior attribute of one box (from S to R). The behavior attributes of the finest-level boxes are shown in Figure 2.



Figure 8. On the left, we show the 3D arrangement of boxes and sequences traversing the 3D object. Two retargeting results of the input at the left of the middle are also shown here.



(a) The inputs are modeled by anartist after real-world buildings.
(b) The inputs are from Google Warehouse.
Figure 9. Retargeting results demonstrating our method’s ability to handle irregular structures and the model variety it generates.



Figure 10. A virtual community set up by the retargeted models.


Thanks: We thank the anonymous reviewers for their valuable suggestions. This work was supported in part by NSFC (60902104, 61025012, 61003190), 863 Program (2011AA010500), CAS One Hundred Scholar Program, CAS Visiting Professorship for Senior International Scientists, CAS Fellowship for Young International Scientists, Shenzhen Science and Technology Foundation (JC201005270329A, JC201005270340A), China Postdoctoral Science Foundation (201104146), the Israel Science Foundation, Lynn and William Frankel Center for Computer Sciences and the Tuman Fund, and the Natural Sciences and Engineering Research Council of Canada (No. 611370).
Bibtex:

@article{Lin_siga11,
    author = {Jinjie Lin and Daniel Cohen-Or and Hao (Richard) Zhang and Cheng Liang and Andrei Sharf and Oliver Deussen and Baoquan Chen},
     title = {Structure-Preserving Retargeting of Irregular 3D Architecture},
     year = {2011},
     month = {dec},
     journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2011)},
     volume = {30},
     number = {6},
     pages = {Article 183}
}