Presentation is loading. Please wait.

Presentation is loading. Please wait.

1/50 Photo-Inspired Model-Driven 3D Object Modeling Kai Xu 1,2 Hanlin Zheng 3 Hao (Richard) Zhang 2 Daniel Cohen-Or 4 Ligang Liu 3 Yueshan Xiong 1 1 National.

Similar presentations


Presentation on theme: "1/50 Photo-Inspired Model-Driven 3D Object Modeling Kai Xu 1,2 Hanlin Zheng 3 Hao (Richard) Zhang 2 Daniel Cohen-Or 4 Ligang Liu 3 Yueshan Xiong 1 1 National."— Presentation transcript:

1 1/50 Photo-Inspired Model-Driven 3D Object Modeling Kai Xu 1,2 Hanlin Zheng 3 Hao (Richard) Zhang 2 Daniel Cohen-Or 4 Ligang Liu 3 Yueshan Xiong 1 1 National Univ. of Defense Tech. 2 Simon Fraser Univ. 3 Zhejiang Univ. 4 Tel-Aviv Univ.

2 2/50 Photo-Inspired Model-Driven 3D Object Modeling 1 National Univ. of Defense Tech. 2 Simon Fraser Univ. 3 Zhejiang Univ. 4 Tel-Aviv Univ. Kai Xu 1,2 Hanlin Zheng 3 Hao (Richard) Zhang 2 Daniel Cohen-Or 4 Ligang Liu 3 Yueshan Xiong 1

3 3/50 3D content creation Inspiration? Inspiration a readily usable digital 3D model

4 4/50 Inspiration = real-world data Inspiration = real-world data [Nan et al., 2010] Realistic reconstruction

5 5/50 Creation of novel 3D shapes Creation of novel 3D shapes Inspiration = design concept, menta l picture, … Inspiration = design concept, menta l picture, … sketch Creative inspiration

6 6/50 3D content creation is hard 2D-to-3D: an ill-posed problem: Shape from shading, sketch-based modeling, … 3D creation from scratch is hard: job for skilled artists One of the most fundamental problems in graphics Jim Kajiyas Award Talk: Geometric modeling still hard! One of the most fundamental problems in graphics Jim Kajiyas Award Talk: Geometric modeling still hard!

7 7/50 Usable 3D content even harder Models created are meant for subsequent use Editing, modification, generation of new models … iWires [Gal et al. 2009]

8 8/50 Usable 3D content even harder Creation of readily usable models Part information (segmentation) or characteristic curves (wires) Structural relations between parts/wires Correspondence among relevant models: co-segmentation, etc. Component-wise controllers [Zheng et al. 2011] iWires [Gal et al. 2011] Co-segmentation [Xu et al. 2010] Hard shape analysis problems, esp. for man-made models

9 9/50 Key: model reuse Reuse pre-existing 3D models Particularly their pre-analyzed structures Segmentation benchmarks [Chen et al. 2009, Kalogerakis et al. 2010] Not only serve to evaluate, but also to create

10 10/50 Key: model reuse Two primary modes of reuse: New creation via part re-composition Modeling by example [Funkhouser et al. 2004] Data-driven part suggestions [Chaudhuri et al., 2010 & 2011] Pre-existing structural information can be lost …

11 11/50 Key: model reuse [Xu et al. 2010] [Kraevoy et al. 2009] Varying part scales Appearance-driven, organic shapes Two primary modes of reuse: New creation via part composition New creation as a variation of existing models, e.g, a warp or deformation

12 12/50 Model-driven 3D content creation Generate variations from a pre-analyzed candidate model set

13 13/50 Photo-inspired 3D modeling Photographs: one of the richest source of modeling inspiration On-line photographs, often only in single-views

14 14/50

15 15/50 Creation readily usable Subsequent model editing

16 16/50 Overview

17 17/50 Pre-analyzed candidate model set Part correspondence [Xu et al. 2010] Input model set Models in part correspondence

18 18/50 Pre-analyzed candidate model set Component-wise controllers [Zheng et al. 2011] Controller primitives: cuboids and generalized cylinders Interrelations: symmetry, proximity, etc.

19 19/50 Overview of our method Step 1: Model-driven image-space object analysis

20 20/50 Model-driven image-space object analysis Retrieval of representative model Model-driven labeled segmentation Graph cut segmentation

21 21/50 Overview of our method Step 2: Candidate model retrieval

22 22/50 Candidate model retrieval Query Top 5 retrieved results whole shape Light Field Descriptor

23 23/50 Candidate model retrieval Query Top 5 retrieved results part-level Light Field Descriptor Candidates may be randomly chosen --- modeling surprise

24 24/50 Overview of our method The key step 3: Silhouette-driven deformation

25 25/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation Four sub-steps:

26 26/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation

27 27/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation

28 28/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation

29 29/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation

30 30/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation

31 31/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation

32 32/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation Before optimization After optimization

33 33/50 Silhouette-driven deformation Silhouette correspondence Initial controller reconstruction Controller optimization Underlying geometry deformation Before optimization After optimization Final geometry

34 34/50 Structure optimization at work Initial controller reconstruction Front-view

35 35/50

36 36/50 ResultsResults Candidate not always chosen as best so as to show the power of silhouette-driven warp

37 37/50 Tables

38 38/50 Lamps

39 39/50 The Google Chair Challenge

40 40/50 The Google Chair Challenge

41 41/50 The Google Chair Challenge

42 42/50 The Google Chair Challenge ?

43 43/50 The Google Chair Challenge

44 44/50 The Google Chair Challenge

45 45/50 Conclusion and limitations Photo-inspired model-driven 3D content creation Utilizes 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 Limitations: Variation does not create new structures, e.g., new connectivity or topology Modeling at the coarse level, refined modeling to follow Resemblance to photographed object is only through silhouette matching Conflicts may occur between constraints to be enforced

46 46/50 Random candidate Conflicting constraints

47 47/50 Future work Photo-inspired model deformation only a start Other inspirations for 3D content creation Sketch-inspired model variation Interior feature curves

48 48/50 Future work Photo-inspired model deformation only a start Other inspirations for 3D content creation Sketch-inspired model variation Interior feature curves Bigger questions A common high-level structural representation, for individual or a set? low-level mesh reps seem like the wrong choice for modeling Easy creation of new structures (topology) that well retain pre-analyzed structures from geometry creation to structure creation

49 49/50 Acknowledgement Anonymous reviewers The authors of [Zheng et al. EG 2011] Aiping Wang from NUDT Grants NSERC (No ) Doctoral Program of Higher Education of China (No ) the Israel Science Foundation National Natural Science Foundation of China ( ) 973 National Key Basic Research Foundation of China (No. 2009CB320801).

50 50/50


Download ppt "1/50 Photo-Inspired Model-Driven 3D Object Modeling Kai Xu 1,2 Hanlin Zheng 3 Hao (Richard) Zhang 2 Daniel Cohen-Or 4 Ligang Liu 3 Yueshan Xiong 1 1 National."

Similar presentations


Ads by Google