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SIGGRAPH Paper Reading 2011
Huang Haibin2011.7.4
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Paper list
• Procedural & Interactive Modeling Interactive Furniture Layout Using Interior Design Guidelines Converting 3D Furniture Models to Fabricatable Parts and Connectors Make it Home: Automatic Optimization of Furniture Arrangement Computer-Generated Residential Building Layouts Interactive Architectural Modeling with Procedural Extrusions Metropolis Procedural Modeling
• Image Processing Domain Transform for Edge-Aware Image and Video Processing Non-Rigid Dense Correspondence with Applications for Image
Enhancement
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Layout Generation
• Interactive Furniture Layout Using Interior Design Guidelines
• Computer-Generated Residential Building Layouts
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Interactive Furniture Layout Using Interior Design Guidelines (SIGGRAPH 2011)
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Paul Merrell
Maneesh Agrawala
Eric Schkufza Zeyang Li
Stanford University
Vladlen Koltun
University of California, Berkeley
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Main idea
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Contributions
• 1. Identify and operationalize a set of design guidelines for furniture layout
• 2. Develop an interactive system for creating furniture arrangements based on these guidelines
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Furniture Layout Guidelines
• 1. Functional Criteria
• 2.Visual Criteria
• 3. Authoring
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Functional Criteria
• 1. Clearance
• 2. Circulation
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• 3. Pairwise relationships
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• 4.Conversation
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Functional Criteria
• 1. Balance
• 2. Alignment
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• 3. Emphasis
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Generation Suggestions
Monte Carlo Sampler
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• Density Function and Sampling
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Results
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Computer-Generated Residential Building Layouts(SIGRAPH Asia 2010)
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Paul Merrell Eric Schkufza Zeyang Li
Stanford University
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Main idea
A list of high-level requirements
Computer-Generated Residential Building Layouts
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Contributions
• Data-driven generation of architectural programs from high-level requirements.
• Fully automated generation of detailed multi-story floor plans from architectural programs.
• An end-to-end approach to automated generation of building layouts from high-level requirements.
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Building Layout Design
• 1. Architectural Programming
• 2.Floor Plan Optimization
• 3. Generating 3D models
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Data- driven Architectural Programming
• 1. Bayesian Networks
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• Structure Learning
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Floor Plan Optimization
• Proposal Moves 1.Notation 2.Sliding a wall 3.Swapping rooms
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Cost Function
• Accessibility
• Dimensions
• Floors
• Shapes
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Generating 3D models
• Passageways
• Windows
• Staircases
• Roofs
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Results
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Procedural & Interactive Modeling
• Interactive Architectural Modeling with Procedural Extrusions
• Metropolis Procedural Modeling
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• 1. Grammar-based: L-system…
• 2. Other: 3DMax…
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Metropolis Procedural Modeling
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Main Idea
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Contribution
• An algorithm for controlling grammar-based procedural models
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Solutions
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Problems
• 1. Generate the space of productions from the grammar
• 2.Define an objective function that quantifies the similarity between a given production and the specification
• 3. Optimization
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PROBABILISTIC INFERENCE FOR GRAMMARS
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LIKELIHOOD FORMULATIONS
• Image- and volume-based modeling
• Mondrian modeling
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Optimization
• MCMC jump MCMC
1.Reversibility
2.Dimension matching
3Acceptance probability
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MCMC FOR GRAMMARS
• Diffusion moves
• Jump moves
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Results
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Interactive Architectural Modeling with Procedural Extrusions
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Main idea
• Model complex architectural features, including overhanging roofs, dormer windows, interior dormer windows, roof constructions with vertical walls, buttresses, chimneys, bay windows, columns, pilasters, and alcoves.
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USER INTERFACE DESCRIPTION
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• Modeling With Profiles• Plans and Profiles• Overhangs• Anchors• Plan Edits• Positioning Procedural Details
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COMPUTING PROCEDURAL EXTRUSIONS
• Generalized Intersection Event• Edge Direction Events• Profile Offset Events• Insertions into the Polygon• Ambiguities in Procedural Extrusions
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Image Processing
• Domain Transform for Edge-Aware Image and Video Processing
• Non-Rigid Dense Correspondence with Applications for Image Enhancement
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Domain Transform for Edge-Aware Image and Video Processing
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Authors
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Main idea
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Contributions
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Transform for Edge-Preserving Filtering
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5D 2D
1D
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Domain Transform
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Application to Edge-Preserving Filtering
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Filtering 2D Signals
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Results
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• Detail Manipulation• Tone Mapping• Stylization• Joint Filtering• Colorization
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Non-Rigid Dense Correspondence with Applications for Image Enhancement
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Authors
Yoav HaCohen Eli Shechtman
Dan GoldmanDani Lischinski
The Hebrew University of Jerusalem
The Hebrew University of JerusalemAdobe Systems
Adobe Systems
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Main idea
• The images are close to each other in time and in viewpoint, and a dense correspondence field may be established using optical flow or stereo reconstruction techniques.
• The difference in viewpoint may be large, but the scene consists of mostly rigid objects
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• The input images share some common content, but may differ significantly due to a variety of factors, such as non-rigid changes in the scene, changes in lighting and tone mapping, and different cameras and lenses.
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Overview
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Nearest-neighbor search
• Based on Generalized PatchMatch algorithm(SIGGRAPH 2010)
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Aggregating consistent regions
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• Global color mapping
• Search constraints
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Evaluation
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Applications
• Local color transfer
• Deblurring
• Mask transfer
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