SIGGRAPH Paper Reading 2011

Huang Haibin2011.7.4

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

Layout Generation

• Interactive Furniture Layout Using Interior Design Guidelines

• Computer-Generated Residential Building Layouts

Interactive Furniture Layout Using Interior Design Guidelines (SIGGRAPH 2011)

Paul Merrell

Maneesh Agrawala

Eric Schkufza Zeyang Li

Stanford University

Vladlen Koltun

University of California, Berkeley

Main idea

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

Furniture Layout Guidelines

• 1. Functional Criteria

• 2.Visual Criteria

• 3. Authoring

Functional Criteria

• 1. Clearance

• 2. Circulation

• 3. Pairwise relationships

• 4.Conversation

Functional Criteria

• 1. Balance

• 2. Alignment

• 3. Emphasis

Generation Suggestions

Monte Carlo Sampler

• Density Function and Sampling

Results

Computer-Generated Residential Building Layouts(SIGRAPH Asia 2010)

Paul Merrell Eric Schkufza Zeyang Li

Stanford University

Main idea

A list of high-level requirements

Computer-Generated Residential Building Layouts

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.

Building Layout Design

• 1. Architectural Programming

• 2.Floor Plan Optimization

• 3. Generating 3D models

Data- driven Architectural Programming

• 1. Bayesian Networks

• Structure Learning

Floor Plan Optimization

• Proposal Moves 1.Notation 2.Sliding a wall 3.Swapping rooms

Cost Function

• Accessibility

• Dimensions

• Floors

• Shapes

Generating 3D models

• Passageways

• Windows

• Staircases

• Roofs

Results

Procedural & Interactive Modeling

• Interactive Architectural Modeling with Procedural Extrusions

• Metropolis Procedural Modeling

• 1. Grammar-based: L-system…

• 2. Other: 3DMax…

Metropolis Procedural Modeling

Main Idea

Contribution

• An algorithm for controlling grammar-based procedural models

Solutions

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

PROBABILISTIC INFERENCE FOR GRAMMARS

LIKELIHOOD FORMULATIONS

• Image- and volume-based modeling

• Mondrian modeling

Optimization

• MCMC jump MCMC

1.Reversibility

2.Dimension matching

3Acceptance probability

MCMC FOR GRAMMARS

• Diffusion moves

• Jump moves

Results

Interactive Architectural Modeling with Procedural Extrusions

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.

USER INTERFACE DESCRIPTION

• Modeling With Profiles• Plans and Profiles• Overhangs• Anchors• Plan Edits• Positioning Procedural Details

COMPUTING PROCEDURAL EXTRUSIONS

• Generalized Intersection Event• Edge Direction Events• Profile Offset Events• Insertions into the Polygon• Ambiguities in Procedural Extrusions

Image Processing

• Domain Transform for Edge-Aware Image and Video Processing

• Non-Rigid Dense Correspondence with Applications for Image Enhancement

Domain Transform for Edge-Aware Image and Video Processing

Authors

Main idea

Contributions

Transform for Edge-Preserving Filtering

5D 2D

1D

Domain Transform

Application to Edge-Preserving Filtering

Filtering 2D Signals

Results

• Detail Manipulation• Tone Mapping• Stylization• Joint Filtering• Colorization

Non-Rigid Dense Correspondence with Applications for Image Enhancement

Authors

Yoav HaCohen Eli Shechtman

Dan GoldmanDani Lischinski

The Hebrew University of Jerusalem

The Hebrew University of JerusalemAdobe Systems

Adobe Systems

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

• 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.

Overview

Nearest-neighbor search

• Based on Generalized PatchMatch algorithm(SIGGRAPH 2010)

Aggregating consistent regions

• Global color mapping

• Search constraints

Evaluation

Applications

• Local color transfer

• Deblurring

• Mask transfer