The PlanarReflective Symmetry Transform Princeton University Motivation Symmetry
- Slides: 69
The Planar-Reflective Symmetry Transform Princeton University
Motivation Symmetry is everywhere
Motivation Symmetry is everywhere Perfect Symmetry [Blum ’ 64, ’ 67] [Wolter ’ 85] [Minovic ’ 97] [Martinet ’ 05]
Motivation Symmetry is everywhere Local Symmetry [Blum ’ 78] [Mitra ’ 06] [Simari ’ 06]
Motivation Symmetry is everywhere Partial Symmetry [Zabrodsky ’ 95] [Kazhdan ’ 03]
Goal A computational representation that describes all planar symmetries of a shape ? Input Model
Symmetry Transform A computational representation that describes all planar symmetries of a shape ? Input Model Symmetry Transform
Symmetry Transform A computational representation that describes all planar symmetries of a shape ? Perfect Symmetry = 1. 0
Symmetry Transform A computational representation that describes all planar symmetries of a shape ? Zero Symmetry = 0. 0
Symmetry Transform A computational representation that describes all planar symmetries of a shape ? Local Symmetry = 0. 3
Symmetry Transform A computational representation that describes all planar symmetries of a shape ? Partial Symmetry = 0. 2
Symmetry Measure Symmetry of a shape is measured by correlation with its reflection
Symmetry Measure Symmetry of a shape is measured by correlation with its reflection Symmetry = 0. 7
Symmetry Measure Symmetry of a shape is measured by correlation with its reflection Symmetry = 0. 3
Symmetry Measure Symmetry of a shape is measured by correlation with its reflection
Symmetry Measure Symmetry of a shape is measured by correlation with its reflection Symmetry = 0. 1
Outline • Introduction • Algorithm – Computing Discrete Transform – Finding Local Maxima Precisely • Applications – Alignment – Segmentation • Summary – Matching – Viewpoint Selection
Computing Discrete Transform n planes • Brute Force • Convolution • Monte-Carlo
Computing Discrete Transform O(n 3) planes X = O(n 6) O(n 3) dot product O(n 6) n planes • Brute Force • Convolution • Monte-Carlo
Computing Discrete Transform O(n 2) normal directions X = O(n 5 log n) O(n 3 log n) per direction O(n 6) O(n 5 Log n) n planes • Brute Force • Convolution • Monte-Carlo
Computing Discrete Transform • Brute Force • Convolution • Monte-Carlo O(n 6) O(n 5 Log n) O(n 4) For 3 D meshes – Most of the dot product contains zeros. – Use Monte-Carlo Importance Sampling.
Offset Monte Carlo Algorithm Angle Input Model Symmetry Transform
Monte Carlo Algorithm Offset Monte Carlo sample for single plane Angle Input Model Symmetry Transform
Offset Monte Carlo Algorithm Angle Input Model Symmetry Transform
Offset Monte Carlo Algorithm Angle Input Model Symmetry Transform
Offset Monte Carlo Algorithm Angle Input Model Symmetry Transform
Offset Monte Carlo Algorithm Angle Input Model Symmetry Transform
Offset Monte Carlo Algorithm Angle Input Model Symmetry Transform
Weighting Samples Need to weight sample pairs by the inverse of the distance between them P 2 d P 1
Weighting Samples Need to weight sample pairs by the inverse of the distance between them Two planes of (equal) perfect symmetry
Weighting Samples Need to weight sample pairs by the inverse of the distance between them Votes for vertical plane…
Weighting Samples Need to weight sample pairs by the inverse of the distance between them Votes for horizontal plane.
Outline • Introduction • Algorithm – Computing Discrete Transform – Finding Local Maxima Precisely • Applications – Alignment – Segmentation • Summary – Matching – Viewpoint Selection
Finding Local Maxima Precisely Motivation: • Significant symmetries will be local maxima of the transform: the Principal Symmetries of the model Principal Symmetries
Finding Local Maxima Precisely Approach: • Start from local maxima of discrete transform
Finding Local Maxima Precisely Approach: • Start from local maxima of discrete transform • Refine iteratively to find local maxima precisely ………. Initial Guess Final Result
Outline • Introduction • Algorithm – Computing discrete transform – Finding Local Maxima Precisely • Applications – Alignment – Segmentation • Summary – Matching – Viewpoint Selection
Application: Alignment Motivation: • Composition of range scans • Feature mapping PCA Alignment
Application: Alignment Approach: • Perpendicular planes with the greatest symmetries create a symmetry-based coordinate system.
Application: Alignment Approach: • Perpendicular planes with the greatest symmetries create a symmetry-based coordinate system.
Application: Alignment Approach: • Perpendicular planes with the greatest symmetries create a symmetry-based coordinate system.
Application: Alignment Approach: • Perpendicular planes with the greatest symmetries create a symmetry-based coordinate system.
Application: Alignment Results: PCA Alignment Symmetry Alignment
Application: Matching Motivation: • Database searching = Query Database Best Match
Application: Matching Observation: • All chairs display similar principal symmetries
Application: Matching Approach: • Use Symmetry transform as shape descriptor = Query Transform Database Best Match
Application: Matching Results: • The PRST provides orthogonal information about models and can therefore be combined with other shape descriptors
Application: Matching Results: • The PRST provides orthogonal information about models and can therefore be combined with other shape descriptors
Application: Matching Results: • The PRST provides orthogonal information about models and can therefore be combined with other shape descriptors
Application: Segmentation Motivation: • Modeling by parts • Collision detection [Chazelle ’ 95][Li ’ 01] [Mangan ’ 99][Garland ’ 01] [Katz ’ 03]
Application: Segmentation Observation: • Components will have strong local symmetries not shared by other components
Application: Segmentation Observation: • Components will have strong local symmetries not shared by other components
Application: Segmentation Observation: • Components will have strong local symmetries not shared by other components
Application: Segmentation Observation: • Components will have strong local symmetries not shared by other components
Application: Segmentation Observation: • Components will have strong local symmetries not shared by other components
Application: Segmentation Approach: • Cluster points on the surface by how well they support different symmetries …. . Support = 0. 1 Support = 0. 5 Support = 0. 9 Symmetry Vector = { 0. 1 , 0. 5 , …. , 0. 9 }
Application: Segmentation Results:
Application: Viewpoint Selection Motivation: • Catalog generation • Image Based Rendering [Blanz ’ 99][Vasquez ’ 01] [Lee ’ 05][Abbasi ’ 00] Picture from Blanz et al. ‘ 99
Application: Viewpoint Selection Approach: • Symmetry represents redundancy in information.
Application: Viewpoint Selection Approach: • Symmetry represents redundancy in information • Minimize the amount of visible symmetry • Every plane of symmetry votes for a viewing direction perpendicular to it Best Viewing Directions
Application: Viewpoint Selection Results: Viewpoint Function
Application: Viewpoint Selection Results: Viewpoint Function Best Viewpoint
Application: Viewpoint Selection Results: Viewpoint Function Best Viewpoint Worst Viewpoint
Application: Viewpoint Selection Results:
Summary • Symmetry Transform – Symmetry measure for all planes in space • Algorithms – Discrete set of planes – Finding local maxima precisely • Applications – Alignment – Matching – Segmentation – Viewpoint Selection
Future Work Extended forms of symmetry • Rotational symmetry • Point symmetry • General transform symmetry Signal processing Further applications • Compression • Constrained editing • Etc.
Acknowledgements: Princeton Graphics • Chris De. Coro • Michael Kazhdan Funding • Air Force Research Lab grant #FA 8650 -04 -1 -1718 • NSF grant #CCF-0347427 • NSF grant #CCR-0093343 • NSF grant #IIS-0121446 • The Sloan Foundation
The End
Comparison Goal Podolak et al. Mitra et al. Transform Discrete symmetry Sampling Uniform grid Clustering Voting Points, normals, curvature Points only Symmetr Planar reflection y Types Reflection/Rotation/etc. Detection Perfect, partial, and Types continuous symmetries Perfect and Partial and approximate symmetries
- Princeton university’s gerrymandering project
- Pics princeton
- Cos316 princeton
- Princeton imaging and analysis center
- Hantao ji
- Cos423
- Cos 423
- Princeton cos217
- Cos 320°
- Princeton physics department
- Eric larson princeton
- Roberto car princeton
- Princeton policy task force
- Cos princeton
- Citp princeton
- Cmr china
- Emil praun
- Kirk mcdonald physics
- Cos 318°
- Computer science certificate princeton
- Tigerpay princeton
- Cs princeton
- Professional headshots princeton
- Amir ali ahmadi
- Chris tully princeton
- Princeton cos
- Measure h
- Avi wigderson princeton
- Princeton cos certificate
- Princeton
- David huse
- Thomas prince school
- Frederik simons princeton
- Pfc princeton
- Avi wigderson
- Hong qin princeton
- Cos418
- Absorbed power
- Nanoimprint
- Hát kết hợp bộ gõ cơ thể
- Ng-html
- Bổ thể
- Tỉ lệ cơ thể trẻ em
- Chó sói
- Tư thế worm breton
- Chúa yêu trần thế
- Các môn thể thao bắt đầu bằng từ đua
- Thế nào là hệ số cao nhất
- Các châu lục và đại dương trên thế giới
- Cong thức tính động năng
- Trời xanh đây là của chúng ta thể thơ
- Cách giải mật thư tọa độ
- Làm thế nào để 102-1=99
- Phản ứng thế ankan
- Các châu lục và đại dương trên thế giới
- Thơ thất ngôn tứ tuyệt đường luật
- Quá trình desamine hóa có thể tạo ra
- Một số thể thơ truyền thống
- Cái miệng xinh xinh thế chỉ nói điều hay thôi
- Vẽ hình chiếu vuông góc của vật thể sau
- Thế nào là sự mỏi cơ
- đặc điểm cơ thể của người tối cổ
- Ví dụ giọng cùng tên
- Vẽ hình chiếu đứng bằng cạnh của vật thể
- Vẽ hình chiếu vuông góc của vật thể sau
- Thẻ vin
- đại từ thay thế
- điện thế nghỉ
- Tư thế ngồi viết
- Diễn thế sinh thái là