Name
Abstract | ||
|---|---|---|
We propose a new method for shape reconstruction from noisy and unorganized point data. We represent a shape through its signed distance function and formulate shape reconstruction as a constrained energy minimization problem directly based on the observed point set. The associated energy function includes both the likelihood of the observed data points and a smoothness prior on the reconstructed shape. To solve this optimization problem, an efficient data-driven level set method is developed. Our method is robust to local minima, clutter, and noise. It is also applicable to situations where the data are sparse. The topological nature of the underlying shape is handled automatically through the level set formalism. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICASSP.2004.1326469 | ICASSP (3) |
Keywords | Field | DocType |
shape reconstruction,unorganized points,noise,data-driven level set method,set theory,image reconstruction,random noise,optimization,signed distance function,clutter,minimisation,energy minimization problem,level set method,level set,distance function,polynomials,surface reconstruction,local minima,noise shaping,data engineering,optimization problem | Data point,Iterative reconstruction,Active shape model,Mathematical optimization,Pattern recognition,Level set method,Signed distance function,Computer science,Level set,Maxima and minima,Shape optimization,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
3 | 1520-6149 | 0-7803-8484-9 |
Citations | PageRank | References |
3 | 0.48 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yonggang Shi | 1 | 598 | 54.47 |
| W. Clem Karl | 2 | 224 | 35.45 |