Data-Driven Shape Analysis --- Geometry...
Transcript of Data-Driven Shape Analysis --- Geometry...
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
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Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
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Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
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Algorithm Overview
Candidate Parts Selection Structure Composition Part Conjoining
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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