TopicsProject Group

WS 2014/2015

Research Staff of

Computer Graphics Departement

Institute of Computer Science II

Computer Graphics

Seminar / Lab / Project Group Topics• Interactive 3D Graphics (Advisor: Christoph Peters, peters@cs...)

Textures• A Testbed for Example-based Texture Synthesis Algorithms (Advisors: Heinz Christian

Steinhausen steinhau@cs..., Dennis den Brok denbrok@cs...)

Animation• Ground detection (Advisor: Dr. Björn Krüger, kruegerb@cs...)

3D Modeling and Analysis• Structure Completion for Facade Layouts (Advisor: Stefan Hartman hartmans@cs..., Elena Trunz,

trunz@cs...)

• Co-Hierarchical Analysis of Shape Structures (Advisor: Elena Trunz, trunz@cs...)

• Exploring Shape Variations by 3D-Model Decomposition and Part-based Recombination (Advisor: Elena Trunz, trunz@cs...)

• Selected Topics in Inverse Procedural Modelling (Advisor: Elena Trunz, trunz@cs...)

Seminar / Lab / Project Group TopicsPoint Cloud Processing• Segmentation of point clouds (Advisor: Fee Bemberg, bemberg@cs...)

• Registration of point clouds (Advisor: Fee Bemberg, bemberg@cs...)

Surface Reconstruction• Continuous Projection for Fast L1 Reconstruction (Advisor: Sebastian Merzbach

merzbach@cs...)

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

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.

Interactive 3D Graphics• Project phases:

1. Project suggestion and pre-production,

2. Implementation of basics,

3. Creating content,

4. Implementation of papers,

5. 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 ☺.

• Texture: Image consisting of repetitive patterns– Locality: Pixel value only depends on

limited neighborhood– Stationarity: For a window of suitable

size, window content is independent of window position in the image

• Example-based synthesis:

A Testbed for Example-basedTexture Synthesis AlgorithmsAdvisors: Heinz Christian Steinhausen (steinhau@cs.uni-bonn.de), Dennis den Brok (denbrok@cs.uni-bonn.de)

Texture?

• Problem: No consistent framework for testing and comparing algorithms available

• Roadmap:1. Analyse some algorithms to find

common parameter structure

2. Implement common framework,incl. benchmarking facilites

3. Implement basic algorithms

4. [Tune algorithms]

5. [Find your own, improved method?]

A Testbed for Example-basedTexture Synthesis AlgorithmsAdvisors: Heinz Christian Steinhausen (steinhau@cs.uni-bonn.de), Dennis den Brok (denbrok@cs.uni-bonn.de)

• Tasks:

• Create common framework

• Implement basic algorithms

• Optimize w.r.t. speed / memory requirements

B PGM SemM Lab

Prof. Dr. A. Weber, Dr. Björn Krüger Lecture Computer Animation, Summer Semester 2014 9

Ground detection

Is it possible todetect the groundyou‘re walking on?

Goal: distinguish grounds for one motion class: Walking or running. On the basis of accelerometer readings.

Related work: B. Krüger et al.

Multi-Mode Tensor Representation of Motion Data

A. Vögele et al.Efficient Unsupervised Temporal Segmentationof Human Motion

B PGM SeminarM Lab

Structure Completion for Facade Layouts

Given: (highly) incomplete facade, database of example facades

Wanted: facade completion

Solution:

Step 1(offline): Training -> statistical model

Step 2 (online): Computation of completed candidate structures

Suitable for a team of two students! 2x B PG M Sem2x M Lab

Co-Hierarchical Analysis of Shape Structures• Unsupervised analysis for structural

hierarchy extraction from 3D shapes– based on unsupervised cluster-and-select

scheme

• Provides correspondences between geometrically dissimilar yet functionally equivalent shape parts across the set– attribute transfer– shape aware editing etc.

B PGM SemM Lab

Exploring Shape Variations by 3D-Model Decomposition and Part-based Recombination• Given: two (or more) objects• Wanted: all meaningful shapes between the given shapes

• First phase: shape analysis– Segmentation of shapes– Contact analysis– Symmetry detection

• Second phase: shape synthesis– Shape matching– Interpolation– Contact enforcement

B PGM SemM Lab

Selected Topics in Inverse Procedural Modeling

• Manual modeling of 2D/3D data is a difficult task

• Idea: – Use already existing objects – Learn a procedural discription (grammar) for this class of objects– Automatically generate various models

• Useful for– Automatic modeling– Compression– Retrieval B PG

M SemM Lab

Selected Topics in Inverse Procedural Modeling

• Facades

• Trees

• Ornaments

• Human Motions

Segmentation of point clouds

• 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

Registration of point clouds

• Registration:• Non-Rigid ICP • Elastic Convolved

ICP• Non-rigid shape

matching (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 PG

M SemM Lab

• 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

Continuous Projection for Fast L1 Reconstruction [Preiner et al., SIGGRAPH 2014]

Advisor: Sebastian Merzbach (merzbach@cs.uni-bonn.de)

B PGM SemM Lab

• 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 using

Kernel Density Estimate (KDE)

reduced comlexity

loss of precision

Robust L1-methods

𝑄𝑘+1=argmin𝑋

{𝐸𝑎𝑡𝑡𝑟𝑎𝑐𝑡 (𝑋 ,𝑃 ,𝑄𝑘 )+𝐸𝑟𝑒𝑝𝑢𝑙𝑠𝑒 ( 𝑋 ,𝑄𝑘 ) }

scales with and

Q

P

outlier

• 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

Continuous Projection for Fast L1 Reconstruction [Preiner et al., SIGGRAPH 2014]

Advisor: Sebastian Merzbach (merzbach@cs.uni-bonn.de)

HEM regularized HEM

• CLOP can also be used for robust normal estimation– use spherical mixtures to determine normal directions

Continuous Projection for Fast L1 Reconstruction [Preiner et al., SIGGRAPH 2014]

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