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Data-Driven Shape Analysis--- Geometry Reconstruction
Qi-xing HuangStanford University
Geometry Reconstruction
• Occlusion
• Material
• Accuracy/Resolution/Range
• Speed
Issues
Data-Driven Reconstruction
Combine data + priors (from existing shapes)
• Structure-aware
– Data from the same shape (enforced by structures)
– Rich literature in the computer vision community
• Global priors
– Find similar shapes
Two topics
Structure-aware
Context-based Surface Completion [SIG’04]
Approach
Symmetry aware [SIG’08]
Symmetry aware [SIG’10]
Symmetry aware [SIG’ 10]
Algorithm Steps
Detection Registration Consolidation Filtering/sampling
Mapping
Semi-automatic Repetition Detection
• Cluster descriptors
• Descriptors configuration
from user query
• Automatic partial
matching [Bokeloh et al. 2009]
Algorithm Steps
Detection Registration Consolidation Filtering/sampling
Mapping
Segmentation and Registration
• Coarse alignment using ICP
– Based on planes and edges (RANSAC)
– Weighed by plane confidence
• Estimate plane confidence
– Area
– Homogeneity
– Point anisotropy
0 1
Algorithm Steps
Detection Registration Consolidation Filtering/sampling
Mapping
Consolidation
• Off-plane: work on planes
– Cluster planes
– Choose representative planes
– Project points to
representatives
• In-plane: work on edges in
each plane
Clustering
Consolidation
Weighted L1 median consolidation
Algorithm Steps
Detection Registration Consolidation Filtering/sampling
Mapping
Filtering of lines based on major axis directions
Partition plane into rectangles
Classify to inliers, outliers
Remove outliers, upsample
Filtering and Upsampling
Results: cylinders consolidation
Combining Images and Shapes [ICCV’11]
Detecting symmetries in the image domain
Structure from motion [TOG’14]
Detect symmetries in 3D
Symmetries are enforced in SFM
Coupled Structure-from-Motion and 3D Symmetry Detection for Urban Facades, TOG’14
Global Shape Prior
Example-Based Scan Completion
Example-Based 3D Scan Completion, SGP’05
Examples
Final Model
Context Models
Deformed Models
Search-Classify [TOG’12]
A Search-Classify Approach for Cluttered Indoor Scene Understanding, TOG’12
Two interleaved problems
What are the objects?
Where are the objects?
• Search
–Propagate /
accumulate
patches
• Classify
–Query
classifier to
detect object
Approach
Overview
Training
Search-Classify
Point-cloud Features– Height-size ratio of BBox
– Aspect ratio of each layer
– Bottom-top, mid-top size ratio
– Change in COM along horizontal slabs
Bh
BdBw
Search-Classify• Starts from seeds
– Random patch triplets
– Remove seeds with low confidence
• Accumulating neighbor patches
– Highest classification confidence
• Stop condition
– Steep decrease in classification confidence
0.65 0.92 0.93 0.88
Seed
Refinement via template fitting• Segmented - classified objects problems
– Overlap, outliers, ambiguities etc.
• Refinement
– Outliers = patches with large distance
Results
Contextual information is no considered
Part-based Shape Reconstruction [TOG’12]
No suitable model!shape retrieval
Structure Recovery by Part Assembly TOG’12
Recover the structure by part assembly
Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
……
Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
……
Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
……
Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
……
Results: Chairs• 70 repository models, 11 part categories
Results: Tables• 61 repository models, 4 part categories
Results: Bicycles
• 38 repository models, 9 part categories
Results: Airplanes
• 70 repository models, 6 part categories
Future directions
• Grammar-based
– Understand the variability of a class of shapes [Hao et al. 14]
• Scene reconstruction/Understanding
• Data-driven dynamic geometry reconstruction
Future Directions
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