Understanding shapes
Fun with shapes
Li Guo
2011.07.04
• Exploration of Continuous Variability in Collections of
3D Shapes (Sig11)
• Characterizing Structural Relationships in Scenes
Using Graph Kernels (Sig11)
• Context-Based Search for 3D Models (SigA10)
• Shape google: Geometric words and expressions for
invariant shape retrieval (TOG11)
• Making Burr Puzzles from 3D Models (Sig11)
• A Geometric Study of V-style Pop-ups: Theories and
Algorithms (Sig11)
• Depixelizing Pixel Art (Sig11)
• Digital Micrography (Sig11)
Exploration of Continuous Variability in
Collections of 3D Shapes
Authors
What(Video)
• Propose a new technique for exploring
unorganized collections of 3D models
Motivation
• 3D models become more and more
• Text-based search
– Many within class
• Navigating directly in descriptor space
– High-dimensional
– Not intuitive
• Example-based retrieval
Related work
• Morphable models and deformation modeling
– Global correspondence detection remains a
challenging open problem
• Exploring shape datasets
– Text keywords
– Proxies
– Example-based search
Selling points
• We present a template-based interface for
exploring collections of similar 3D models via
constrained direct manipulation.
• We introduce a novel technique to convert
descriptor variability into a deformation
model for a template shape without relying on
correspondences between shapes.
Overview
Descriptor variability and
template deformations
Shape
Template
deformation
Shape
descriptor
PCA basis
PCA basis
Deformation
Space
Shape descriptor
Template selection and
deformation space
• Template selection
– Order the shapes by the distance to the average
descriptor
– Filter the shapes have many components
• Deformation space
– Template shape with C components
– 6C deformation parameters(3 translation and 3
scaling)
Exploration interface
Results
Future work
• An explicit encoding of the part connectivity
• A convex formulation of a similar optimization
problem
• Outlier detection for shape retrieval
• Analyzing the relation of discrete variability in
the shape
• Extensions to our exploration interface
Characterizing Structural Relationships
in Scenes Using Graph Kernels
Authors
?
What
• Represent scenes as graphs that encode
models and their semantic relationships
• Applications
– Finding similar scenes
– Relevance feedback
– Context-based model search
Motivation
• Scene comparison
Related work
• 3D Model Search
• Scene Comparison
– [Harchaoui and Bach 2007] Image comparison
Spatial Relationships
Representing Scenes As Graphs
• Enclosure, Horizontal Support, Vertical
Contact, Oblique Contact
Graph Comparison
•
•
•
•
Node Kernel
Edge Kernel
Graph Kernel: [Harchaoui and Bach 2007]
Embedding the graphs in a very high
dimensional feature space and computing an
inner product
Dataset
• Google 3D Warehouse
– Most have scene graph
– Standardize the tagging and segmentation (mimics
the method such as PASCAL,MSRC, and LabelMe
[Russell et al. 2008]
Application:
Relevance feedback
Find Similar Scene
Context-based model search
Comparison
Limitations
• Simple relationship
• Many scenes were not reasonably segmented
Future work
• Software that is aware of the relationships
expressed in 3D scenes has significant
potential to augment the scene design
process.
Context-Based Search for 3D Models
Authors
What
• Context search
Motivation
• 3D model search
• Scene modeling
• The goal of this research is to develop a
context-based 3D search engine
Related work
• Geometric Search Engines
• Spatial Context in Computer Vision
– The context challenge
Dataset
• Google 3D Warehouse
– Most have scene graph
– Standardize the tagging and segmentation (mimics
the method such as PASCAL,MSRC, and LabelMe
[Russell et al. 2008]
Overview
• Observations
– All pairs of object co-occurrence across all scenes
• Spatial Relationships
• Object Similarity
• Model Ranking
Results
Benefit of additional supporting objects
Comparing results with and without database tags
Failure Cases
• Geometrically very similar to a relevant object
but semantically very different
• Spatial relationships are overly simplistic
Future work
• Extracting more meaningful spatial
relationships between objects
• Intelligently perform complex actions(意识流)
Shape Google: geometric words and
expressions for invariant shape retrieval
Authors
Alex M. Bronstein
Michael M. Bronstein
LEONIDAS J.
GUIBAS
MAKS
OVSJANIKOV
What
• Non-rigid shape search and retrieval
Motivation
• The same as before
Related work
• [Ovsjanikov et al. 2009] First introduced
– Shape Google: a computer vision approach to invariant
shape retrieval.
• [Sun et al. 2009]
– Feature detector and descriptor based on heat kernels
• [Behmo et al. 2008]
– Taking into consideration the spatial relations between
features
• [Jain et al. 2008]
– Represent shapes as compact binary codes
Feature-based methods in
computer vision
• Feature detection and feature description
Overview
Results
Conclusion
• Non-rigid shape retrieval
– In text retrieval methods’ spirit
– Drew analogies with feature-based image
representations used in the computer vision
Making Burr Puzzles from 3D Models
Authors
?
?
What
• Burr Puzzle: 鲁班锁，孔明锁
– 用一种咬合的方式把木条垂直相交固定
鲁班锁
Overview
Multi-Knot Burr Puzzle
Connection types of neighboring knots
Illustrating the puzzle disassembly
Results
A Geometric Study of V-style Pop-ups:
Theories and Algorithms
Authors
?
?
What (Video)
Definition
• Scaffold
– A collection of planar polygons, called patches,
that are connected at straight line segments
• V-scaffold
– A scaffold where each patch is labelled as either
G,B, L,R
Essential and intriguing properties
of a pop-up
• The pop-up can be closed down to a flat surface and opened
up again without tearing the paper or introducing new
creases other than those in the design.
• The closing and opening of the pop-up do not need extra
forces other than holding and turning the two book pages.
• The paper does not intersect during closing or opening.
• When closed, all pieces of the pop-up are enclosed within the
book page.
Related work
• Paper crafting
• Computational pop-ups
Theoretical foundation:
Double-patch mechanisms
Theoretical foundation:
Single-patch mechanisms (Video)
Main contribution
• A theoretical study of the geometric structure
of v-style pop-ups
• Algorithmic contributions
– An interactive tool for creating v-style pop-ups
– An automated algorithm for constructing a v-style
pop-up from a given 3D model
Interactive design
• An interactive tool using the mechanisms
discussed above
• At each step, the tool makes automated
suggestions of possible locations for adding
patches
Video
Automated construction
• Input: a collection of voxels
• Three steps
– Patches are first constructed to cover the exterior
faces of V parallel to Z axis. (S1, D2)
– Patches covering exterior faces oriented towards
the positive Z axis are added. (D1)
– The ground and the backdrop of the scaffold are
determined
Future work
• On the theoretical end
– Improve the stability conditions
– Considering the physical properties of the paper
• On the algorithmic side
– Provide more intuitive popup design tools
Depixelizing Pixel Art
Authors
What
• Pixel art: digital art where the details in the
image are represented at the pixel level
– Video games before the mid-1990s
– Icons in older desktop environments
• Convert pixel art images to a resolutionindependent vector representation
Related work
• General Image Upsampling
• Pixel Art Upscaling Techniques
– pixel-based and upscale the image by a fixed
integer factor
• Image Vectorization
Motivation
• Conventional image upsampling and
vectorization algorithms cannot handle pixel
art images well
Overview
Results
Limitations
• Closer to natural images
• Splines sometimes smooth certain features
too much
Future work
• In real-time manner
• Improve the handling of anti-aliased input
images
• Temporal upsampling of animated pixel art
images
Digital Micrography
Authors
What
Text layout (main goal)
Relaxing the alignment constraint
• Our challenge is to balance alignment with
readability by selectively relaxing the
alignment constraint.
Related work
• Text Art
• Non-textual layout
• Layout using vector fields
Selling points
• Key technical component of our work is the
introduction of a novel approach for designing
boundary conditions for vector fields
Algorithm Overview
Boundary Conditions Design
Results
Future work
• Expand the range of styles supported by the
framework
• Text line ordering
• Accelerating the method
Thank you