Monday, April 19, 2010

Building a three-dimensional model

One point to resolve is building a three-dimensional model from a given cloud of points, next we present papers that resolve distinct steeps in this process.

here present the problem segmented into patches each represent a discrete surface region on the physical object:
-Physical Design Model
-Digitize Model
-Cloud Data Set
-Apply Reverse Engineering Software
-Comuter based Design Model

Initialy utilize a laser-based range sensor to obtain the cloud data, then presents triangulation method and growth rules to build the mesh.

  • The complexity of the sample data has problems in: size (we need much memory to compute all data), quality (noise in the process of generate the samples) , to overcome this problems we can reduce or simplify the cloud points, this process before build the model could be made by algorithms,

In the paper Efficient Simplification of Point-Sampled Surfaces the methods presented to resolve the pre-processing problem are:

-clustering methods, split the point cloud in subsets, each subset is replaced by one representive.

-Iterative simplification, successively collapses point pairs in a point cloud according to a quadric error metric.

-particle simulation, computes new sampling positions by moving particles on the point-sampled surface according to interparticle repelling forces.

There compare and analyze each algorthms whit emphasis on efficiency and low memory footprint.

This approach is based on the representation of free-form surfaces, by building deferent meshes from each view, a curvature measure is computed at every node of the meshes and map to a spherical image.

Each mesh is represented whit a graph, each node has three neighbors and its curvature is computed from the relative position of its neighbours.

In Resume in this studies, we could review some mehtods and techniques that resolve distinct aspects in the building of a 3D model, is an introduction on the study of the problem.

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