1. Student: Shunan Shi Professor: Hao Zhang CMPT888

2. Content Background Information Pre-processing Key Methods Conclusion & Discussion

3. Part One Background Information 01

4. WHY ： One of the most fundamental tasks in Computer Graphic is pure creation. Turning everything into 3D within seconds. All start with 2D to 3D conversion problems. 1-1 Definition For what ： Establish 3D data base. Design in Architectural and Arts. Satellite Images. Entertainment industry. Problems ： 2D-to-3D: an ill-posed problem, because one dimension of signal will totally loss and computer wont be able to create them from nothing. How ： ?

5. 1-2Pervious Method 1-2 Pervious Method Modeling Scratch Data Shape Sketch-based modeling The main challenge lies in how to meaningfully infer the underlying 3D geometry from just a few casual strokes. In this work, we also use silhouette contours in the input photo to guide the structure preserving deformation. Modeling from single images. Photographs are much easier to acquire using cameras or from online resources. 3D shape reconstruction from 2D images has been an extensively studied problem in computer vision. Data-driven object modeling. Perhaps the most classic approach is to compose parts from existing models. A new model can be created by replacing some of its parts by other similar parts retrieved from a large database.

6. 1-3 Main Steps This paper introduce an algorithm for creative 3D modeling by a single photograph. The proposed method comes in Three parts: 1. Model-driven image-space object analysis. 2. Candidate model retrieval. 3. Silhouette-driven deformation. In this presentation, 1 and 2 will show in Part Two, 3 will show in Part Three. Main Steps

7. Part Two Pre-processing 02

8. Model-Driven Ready the candidate model to perform part correspondence and subsequently normalize the each part scales of each model for following steps. Candidate model set. Search the similar data in our 3D database compare with our input image. The data only needs to roughly match the input object. Retrieval of representative model. To effectively guide the graph-cut properly. We rely on user interaction to achieve this. Model-driven labeled segmentation. 2-1 Model-driven

9. 2-2 Model-driven Retrieval of representative model Model-driven labeled segmentation Graph cut segmentation

10. 2-3 Candidate Query Top 5 retrieved results

11. Part Three Key Methods ： Key Methods ： 03

12. 1. Silhouette correspondence It’s more like a retargeting processing, The difference is the key is to find the correspondence between input chairs and candidate chairs. 2. Initial controller reconstruction Try to “recover” the 3D position of input by orthogonally projecting it onto a supporting plane for in 3D space. Compute the new medial axis, by tracing along the midpoints between the two silhouette segments. 3.Controller optimization This step is to deal with the joints of 3D model that are not well Connected. Respected constraints and Iterative optimization has been used on this steps. 4.Underlying geometry deformation Finally, we transfer the deformation applied to the controllers to the underlying geometry 3-1 Four sub-steps

13. 3-2 Silhouette 1. Silhouette correspondence

14. 3-3 Initial controller 2. Initial controller reconstruction

15. 3-4 Optimization After optimization 3. Controller optimization

16. 3-5 Deformation After optimization 4. Underlying geometry deformation

17. Part Four Conclusion & Discussion 04

18. 4-1 Discussion  Two rich sources: photo inspirations and pre-analyzed 3D models.  Structure-driven image analysis and silhouette-based deformation.  Readily usable: variation less “intrusive” to retain pre-analyzed structures Conclusion ： Variation does not create new structures. Computer is not thinking. And Modeling still at the rough level. Conflicts may occur between constraints. Limitations:

19. Thank You Student: Shunan Shi Professor: Hao Zhang CMPT888