gwy15 / Moving-Least-Squares

Implementation of three algorithms of image deformation using moving least squares. http://dl.acm.org/citation.cfm?doid=1179352.1141920

Moving Least Squares (MLS)

Introduction

Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.

In computer graphics, the moving least squares method is useful for reconstructing a surface from a set of points. Often it is used to create a 3D surface from a point cloud through either downsampling or upsampling.

Methods

• Affine deformation (inverse)
• Similarity deformation (inverse)
• Rigid deformation (inverse)

Preview

• Toy

• Monalisa

• Cells

Code list

• img_utils.py: Implementation of the algorithms
• img_utils_demo.py: Demo program
• read_tif.py: TIF file reader
• tiff_deformation.py: Demo program

Reference

[1] Schaefer S, Mcphail T, Warren J. Image deformation using moving least squares[C]// ACM SIGGRAPH. ACM, 2006:533-540.