IEEE Transactions on Systems, Man and Cybernetics
Through-the-lens camera control
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Robust incremental optical flow
Robust incremental optical flow
A graphics toolkit based on differential constraints
UIST '93 Proceedings of the 6th annual ACM symposium on User interface software and technology
Multiresolution analysis of arbitrary meshes
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Synthesizing realistic facial expressions from photographs
Proceedings of the 25th annual conference on Computer graphics and interactive techniques
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Rank-deficient and discrete ill-posed problems: numerical aspects of linear inversion
Reconstruction and representation of 3D objects with radial basis functions
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Turning to the masters: motion capturing cartoons
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Modelling with implicit surfaces that interpolate
ACM Transactions on Graphics (TOG)
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Near-Optimal Parameters for Tikhonov and Other Regularization Methods
SIAM Journal on Scientific Computing
Building efficient, accurate character skins from examples
ACM SIGGRAPH 2003 Papers
Rank degeneracy and least squares problems
Rank degeneracy and least squares problems
SMI '04 Proceedings of the Shape Modeling International 2004
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Texture optimization for example-based synthesis
ACM SIGGRAPH 2005 Papers
Gamut Constrained Illuminant Estimation
International Journal of Computer Vision
Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
Robust kinematic constraint detection for motion data
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Technical Section: A novel constrained texture mapping method based on harmonic map
Computers and Graphics
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
Rigid shape interpolation using normal equations
NPAR '08 Proceedings of the 6th international symposium on Non-photorealistic animation and rendering
Automatic linearization of nonlinear skinning
Proceedings of the 2009 symposium on Interactive 3D graphics and games
Robust principal axes determination for point-based shapes using least median of squares
Computer-Aided Design
Computer Aided Geometric Design
Physics-inspired upsampling for cloth simulation in games
ACM SIGGRAPH 2011 papers
Edge-constrained image compositing
Proceedings of Graphics Interface 2011
Robust shape normalization of 3D articulated volumetric models
Computer-Aided Design
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The course presents an overview of the least-squares technique and its variants. A wide range of problems in computer graphics can be solved using the least-squares technique (LS). Many graphics problems can be seen as finding the best set of parameters for a model given some data, and usually these parameters can be estimated using a least-squares approaches. For instance, a surface can be determined using data and smoothness penalties, a trajectory can be predicted using previous information, joint angles can be determined from end effector positions, etc. All these problems and many others can be formulated as minimizing the sum of squares of the errors. Despite this apparent versatility, solving problems in the least-squares sense can produce poor results. This occurs when the nature of the problem error does not match the assumptions of the least-squares method. The course explains these assumptions and show how to circumvent some of them to apply LS to a wider range of problem. The focus of the course is to provide a practical understanding of the techniques. Each technique will be explained using the simple example of fitting a line through data, and then illustrated through its use in one or more computer graphics papers.