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Primitive fitting WebGL demo (2D)

Checkout our live demo demonstrating Grenaille's fitting capabilities on 2D points.

Techniques currently implemented:

  • Oriented Sphere Fit: fits algebraic sphere on point clouds with oriented normals,
  • Mean Plane Fit: compute a plane from the mean position and mean normal orientation of a point cloud with oriented normals.


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Growing Least Squares: Analysis of Manifolds in Scale-Space

Growing Least Squares (GLS) is an approach to the multi-scale analysis of point-sampled manifolds of co-dimension 1. It is based on a variant of Moving Least Squares, whereby the evolution of a geometric descriptor at increasing scales is used to locate pertinent locations in scale-space, hence the name “Growing Least Squares”. Compared to existing scale-space analysis methods, this approach is the first to provide a continuous solution in space and scale dimensions, without requiring any parametrization, connectivity or uniform sampling. An important implication is that it identifies multiple pertinent scales for any point on a manifold, a property that had not yet been demonstrated in the literature. In practice, this approach exhibits an improved robustness to change of input, and is easily implemented in a parallel fashion on the GPU.

More details: Project page.


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RayTraced Curvature in Modo

Grenaille is used in Modo (since 801), to estimate object-space curvature in real-time at the rendering stage. The underlying technique has been presented at Siggraph 2013.

More details: Project page.


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Molecule analysis

Grenaille has been used by Noam Kremen (Technion - Israel Institute of Tehcnology, under the supervision of Yael Mandel-Gutfreund) for the project: Predicting protein function based on the local differential geometric properties of its molecular surface.

Get more details at: Coming soon...


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Point-Cloud Multiscale Editing

The Growing Least Squares analysis technique implemented in Grenaille has been used to extract and edit surface features in large point-clouds or meshes. This was made possible by a novel multi-scale analysis of unstructured point-clouds that automatically extracts the number of relevant features together with their respective scale all over the surface. Then, combining this ingredient with an adequate multi-scale decomposition allows us to directly enhance or reduce each feature in an independent manner. This tool may be applied either globally or locally, and millions of points are handled in real-time. The resulting system enables users to accurately edit complex geometries with minimal interaction.

Get more details at: Coming soon...