Name
Abstract | ||
|---|---|---|
Scanners and other acquisition devices are commonly used to capture shapes of real objects as point clouds. Piecewise linear approximations of point clouds are then obtained by reconstruction algorithms. Generic reconstruction algorithms rely on assumptions like smoothness, uniform or dense sampling, etc. In general, they are unable to precisely regenerate salient features of the underlying model like edges and corners. We restrict our focus to engineered models that are formed by components having simple analytically defined shapes like planes, surfaces of revolution and extrusion. We employ a parameterization that allows linearization of constraints to recover these shapes. The regularity and relationships of the components are exploited to reconstruct the accurate shape with sharp edges from highly noisy point clouds. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2425333.2425377 | ICVGIP |
Keywords | Field | DocType |
reconstruction algorithm,accurate reconstruction,engineered model,acquisition device,point cloud,generic reconstruction algorithm,accurate shape,noisy point cloud,piecewise linear approximation,real object,dense sampling,cad,3d reconstruction,reverse engineering | Computer vision,Surface of revolution,Parametrization,Computer science,Reverse engineering,Artificial intelligence,Smoothness,Point cloud,Linearization,Salient,3D reconstruction | Conference |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nithi Gupta | 1 | 0 | 0.68 |
| Subodh Kumar | 2 | 527 | 49.65 |