Guillermo (Guille) D. Canas
Ph.D. candidate. Computer Science
Adviser: Steven (Shlomo) J. Gortler

School of Engineering and Applied Sciences
Harvard University
33 Oxford St. Cambridge, MA. 02138
(510) 384-9601.


Research:

  • Guillermo D. Canas, Yuriy Vasilyev, Yair Adato, Todd Zickler, Steven J. Gortler, and Ohad Ben-Shahar. A Linear Formulation of Shape from Specular Flow. International Conference on Computer Vision 2009. Kyoto, Japan.

    Abstract    . We show that a suitable reparameterization leads to a linear formulation of the shape from specular flow equation. This formulation radically simplifies the reconstruction process and allows, for example, both motion and shape to be recovered from as few as two specular flows even when no externally-provided initial conditions are available.

  • Guillermo D. Canas, Yuriy Vasilyev, Yair Adato, Todd Zickler, Steven J. Gortler, and Ohad Ben-Shahar. Unique specular shape from two specular flows. Harvard SEAS Tech. Report 07-09.


  • Guillermo D. Canas and Steven J. Gortler. Shape Operator Metric for Surface Normal Approximation. 18th International Meshing Roundtable 2009.

    Abstract    . This work deals with the problem of practical mesh generation for surface normal approximation. Part of its contribution is in presenting previous work in a unified framework. A new algorithm for surface normal approximation is then introduced which produces better approximations of surfaces both in practice and in the theoretical limit regime, and resolves some of the problems that previous methods for surface approximation suffered from in a simple way.

  • Guillermo D. Canas and Steven J. Gortler. On Asymptotically Optimal Meshes by Coordinate Transformation. 15th International Meshing Roundtable 2006. [pdf | slides]

    Abstract    . We begin by providing simpler proofs of previously known results in the approximation of functions w.r.t. gradient error and show constructively that a closed-form solution exists for them. We then show that the transformational method for obtaining meshes, as is, cannot produce asymptotically optimal meshes for general inputs. We discuss possible variations of the problem that may allow for some forms of optimality to be proved.

  • Guillermo D. Canas and Steven J. Gortler. Surface Remeshing in Arbitrary Codimensions. Pacific Graphics 2006. [pdf | slides]

    Abstract    . We remesh in three simple stages. First the input surface is mapped to a (possibly high-dimensional) space; then it is uniformly remeshed in that space; and finally brought back to the original space. The resulting mesh derives its properties from the mapping, and can be computed efficiently.

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Last update: 07/10/2009