| Title: | Curvature-Based Regularization for Surface Approximation |
| Fulltext: |  |
| Authors: | Olsson, Carl and Boykov, Yuri |
| Year: | 2012 |
| Document Type: | Conference Paper |
| Conference: | CVPR 2012 |
| Conference location: | Providence, Rhode Island, USA |
| Status: | Published |
| Refereed: | Yes |
| Keywords: | globalvision, ffl |
| BibTeX item: |  |
| Abstract: | We propose an energy-based framework for approximating surfaces from a cloud of point measurements corrupted by noise and outliers. Our energy assigns a tangent plane to each (noisy) data point by minimizing the squared distances to the points and the irregularity of the surface implicitly defined by the tangent planes. In order to avoid the well-known "shrinking" bias associated with first-order surface regularization, we choose a robust smoothing term that approximates curvature of the underlying surface. In contrast to a number of recent publications estimating curvature using discrete (e.g. binary) labellings with triple-cliques we use higher-dimensional labels that allows modeling curvature with only pair-wise interactions. Hence, many standard optimization algorithms (e.g. message passing, graph cut, etc) can minimize the proposed curvature-based regularization functional. The accuracy of our approach for representing curvature is demonstrated by theoretical and empirical results on synthetic and real data sets from multi-view reconstruction and stereo. |
Similar Publications
- Conference paper:
A Convex Approach to Low Rank Matrix Approximation with Missing Data
Carl Olsson
Magnus Oskarsson - Doctoral thesis:
Global Optimization in Computer Vision: Convexity, Cuts and Approximation Algorithms
Carl Olsson - Licentiate's thesis:
Global Optimization and Approximation Algorithms in Computer Vision
Carl Olsson
Questions: webmaster
Last updated: 2012-01-17
Centre for Mathematical Sciences, Box 118, SE-22100, Lund. Phone: 046-222 00 00