Topics Project Group WS 2014/2015

1. TopicsProject GroupWS 2014/2015 Research Staffof Computer Graphics Departement Institute of Computer Science II Computer Graphics

2. Seminar / Lab / Project Group Topics • Interactive 3D Graphics (Advisor: Christoph Peters, peters@cs...) Textures • A TestbedforExample-basedTexture Synthesis Algorithms(Advisors: Heinz Christian Steinhausen steinhau@cs..., Dennis den Brokdenbrok@cs...) Animation • Grounddetection(Advisor: Dr. BjörnKrüger, kruegerb@cs...) 3D Modeling and Analysis • StructureCompletionforFacade Layouts (Advisor: Stefan Hartman hartmans@cs..., Elena Trunz, trunz@cs...) • Co-Hierarchical Analysis of Shape Structures(Advisor: Elena Trunz, trunz@cs...) • Exploring Shape Variationsby 3D-Model Decompositionand Part-basedRecombination(Advisor: Elena Trunz, trunz@cs...) • Selected Topics in Inverse Procedural Modelling (Advisor: Elena Trunz, trunz@cs...)

3. Seminar / Lab / Project Group Topics Point Cloud Processing • Segmentation ofpointclouds(Advisor: Fee Bemberg, bemberg@cs...) • Registration ofpointclouds(Advisor: Fee Bemberg, bemberg@cs...) Surface Reconstruction • ContinuousProjectionfor Fast L1 Reconstruction(Advisor: Sebastian Merzbachmerzbach@cs...)

4. Interactive 3D Graphics • Games, apps, VR, demos and interactive visualizations are important applications of computer graphics. • The underlying technology builds upon strong practical skills: • Native programming languages (e.g. C++, C, objective-C), • Graphics APIs (e.g. Direct3D, OpenGL), • Algorithmic know-how (e.g. spatial queries, mesh processing, matrix math). • Learning by doing works best! B PGM SemM Lab

5. Interactive 3D Graphics • You suggest projects. The rules are: • Work in teams of 2 or 3 students. • Start from scratch using a graphics API rather than an engine. • Interactive 3D graphics are a must have. • Don’t be megalomaniac. Keep it simple. • For every accepted project one or more papers will be suggested to be implemented as part of the project. • Deliver a documentation with a focus on the given papers and hold a final talk. • Regular meetings required. • Use our SVN or git. • We are around to help you with problems.

6. Interactive 3D Graphics • Project phases: • Project suggestion and pre-production, • Implementation of basics, • Creating content, • Implementation of papers, • Documentation and talk. • Warning: • Can be a lot of work, • Strong motivation required, • Should have experience with C++, C or objective-C. • Offered for the first time. • But it is fun ☺.

7. A TestbedforExample-basedTexture Synthesis Algorithms • Texture: Image consistingof repetitive patterns • Locality: Pixel valueonlydepends on limited neighborhood • Stationarity:For a windowofsuitablesize, windowcontentisindependentofwindowposition in theimage • Example-basedsynthesis: Advisors: Heinz Christian Steinhausen (steinhau@cs.uni-bonn.de), Dennis den Brok (denbrok@cs.uni-bonn.de) Texture?

8. A TestbedforExample-basedTexture Synthesis Algorithms • Problem: Noconsistentframeworkfortestingandcomparingalgorithmsavailable • Roadmap: • Analyse somealgorithmsto find commonparameterstructure • Implementcommonframework,incl. benchmarkingfacilites • Implementbasicalgorithms • [Tune algorithms] • [Find yourown, improvedmethod?] Advisors: Heinz Christian Steinhausen (steinhau@cs.uni-bonn.de), Dennis den Brok (denbrok@cs.uni-bonn.de) • Tasks: • Create commonframework • Implementbasicalgorithms • Optimize w.r.t. speed/ memoryrequirements B PGM SemM Lab

9. B PGM SeminarM Lab Grounddetection • Isitpossibletodetectthegroundyou‘rewalking on? • Goal: distinguishgroundsforonemotionclass: • Walking orrunning. • On thebasisofaccelerometerreadings. • Relatedwork: • B. Krüger et al.Multi-Mode Tensor Representationof Motion Data • A. Vögele et al.Efficient Unsupervised Temporal Segmentationof Human Motion

10. StructureCompletionforFacade Layouts Given: (highly) incompletefacade, databaseofexamplefacades Wanted: facadecompletion Solution: Step 1(offline): Training -> statisticalmodel Step 2 (online): Computationofcompletedcandidatestructures Suitablefor a teamoftwostudents! 2x B PG M Sem2x M Lab

11. Co-Hierarchical Analysis of Shape Structures • Unsupervisedanalysisforstructuralhierarchyextractionfrom 3D shapes • based on unsupervisedcluster-and-selectscheme • Providescorrespondencesbetweengeometricallydissimilaryetfunctionallyequivalentshapepartsacrosstheset • attributetransfer • shapeawareediting etc. B PGM SemM Lab

12. Exploring Shape Variationsby 3D-Model Decompositionand Part-basedRecombination • Given: two (ormore) objects • Wanted: all meaningfulshapesbetweenthegivenshapes • First phase: shapeanalysis • Segmentation ofshapes • Contactanalysis • Symmetrydetection • Second phase: shapesynthesis • Shape matching • Interpolation • Contactenforcement B PGM SemM Lab

13. Selected Topics in Inverse Procedural Modeling • Manual modelingof2D/3D datais a difficulttask • Idea: • Usealreadyexistingobjects • Learn a proceduraldiscription (grammar) forthisclassofobjects • Automaticallygeneratevariousmodels • Usefulfor • Automaticmodeling • Compression • Retrieval B PGM SemM Lab

14. Selected Topics in Inverse Procedural Modeling • Facades • Trees • Ornaments • Human Motions

15. Segmentation ofpointclouds Point cloud (3D) Growth Segmentation • Graph-cut • Surface Feature Classification Analyzing Growing Plants from 4D Point Cloud Data [Li et al] Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping [Paulus et al] B PGM SemM Lab

16. Registration ofpointclouds Registration: • Non-Rigid ICP • ElasticConvolved ICP • Non-rigid shapematching (GHD) Introduction: Non-Rigid Registration [Hao Li] Elastic convolved ICP for the registration of deformable objects [Sagawa et al] A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching [Bronstein et al] B PGM SemM Lab

17. Continuous Projection for Fast L1 Reconstruction [Preiner et al., SIGGRAPH 2014] • so far: • L2 methods (PCA, RBFs, MLS, Poisson reconstruction, ...): • fast, real-time-capable • not robust to outliers • oversmoothing • L1 methods: • robust • not real-time-capable Advisor: Sebastian Merzbach (merzbach@cs.uni-bonn.de) B PGM SemM Lab

18. Robust L1-methods • Locally Optimal Projection (LOP): • original points P define attractive forces • iteratively apply forces to resampling points Q • robust, parallelizable • but not real-time-capable  • high cost due to high complexityof all mutual forces • Weighted LOP: • same principle • account for changes in density • more evenly sampled reconstructions • Kernel LOP: • subsample point cloud usingKernel Density Estimate (KDE) • reduced comlexity • loss of precision  scales with and  outlier P Q

19. Continuous Projection for Fast L1 Reconstruction [Preiner et al., SIGGRAPH 2014] • idea: Continuous LOP (CLOP): • consider P as discrete carriers of energy potential • Can we represent more compactly and efficiently? • use Gaussian Mixture Model (GMM) to represent point density • GMM: • represent points in P as a set of Gaussians, • use GMM to compute continuous force field during the LOP iterations • compute GMM via hierarchical expectation-maximization (HEM) scheme • reduce number M of Gaussians by repeated subsampling over the EM-iterations • standard EM not robust • use geometrically regularized HEM Advisor: Sebastian Merzbach (merzbach@cs.uni-bonn.de) HEM regularized HEM

20. Continuous Projection for Fast L1 Reconstruction [Preiner et al., SIGGRAPH 2014] • CLOP can also be used for robust normal estimation • use spherical mixtures to determine normal directions Advisor: Sebastian Merzbach (merzbach@cs.uni-bonn.de) • Tasks (B PG / M Lab): • implementation • HEM to compute GMM • (W)LOP using GMM • normal estimation • evaluate performance and quality on e.g. Kinect data B PGM SemM Lab