IEEE Transactions on Pattern Analysis and Machine Intelligence
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Surface simplification using quadric error metrics
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Shape transformation using variational implicit functions
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
New quadric metric for simplifiying meshes with appearance attributes
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Out-of-core simplification of large polygonal models
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
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 conference on Visualization '01
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
A Multi-scale Approach to 3D Scattered Data Interpolation with Compactly Supported Basis Functions
SMI '03 Proceedings of the Shape Modeling International 2003
Multi-level partition of unity implicits
ACM SIGGRAPH 2003 Papers
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Curvature Dependent Polygonization of Implicit Surfaces
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Robust Particle Systems for Curvature Dependent Sampling of Implicit Surfaces
SMI '05 Proceedings of the International Conference on Shape Modeling and Applications 2005
Spectral sampling of manifolds
ACM SIGGRAPH Asia 2010 papers
| Hi-index | 0.00 |
We present a novel cost function to prioritize points and subsample a point set based on the dominant geometric features and local sampling density of the model. This cost function is easy to compute and at the same time provides rich feedback in the form of redundancy and non-uniformity in the sampling. We use this cost function to simplify the given point set and thus reduce the CSRBF (Compactly Supported Radial Basis Function) coefficients of the surface fit over this point set. Further compression of CSRBF data set is effected by employing lossy encoding techniques on the geometry of the simplified model, namely the positions and normal vectors, and lossless encoding on the CSRBF coefficients. Results on the quality of subsampling and our compression algorithms are provided. The major advantages of our method include highly efficient subsampling using carefully designed, effective, and easy compute cost function, in addition to a very high PSNR (Peak Signal to Noise Ratio) of our compression technique relative to other known point set subsampling techniques.