Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6937380 | Computer Vision and Image Understanding | 2018 | 11 Pages |
Abstract
To reconstruct surface from unorganized points in three-dimensional Euclidean space, we propose a novel efficient and fast method by using l0 gradient minimization, which can directly measure the sparsity of a solution and produce sharper surfaces. Therefore, the proposed method is particularly effective for sharpening major edges and removing noise. Unlike the Poisson surface reconstruction approach and its extensions, our method does not depend on the accurate directions of normal vectors of the unorganized points. The resulting algorithm is developed using a half-quadratic splitting method and is based on decoupled iterations that are alternating over a smoothing step realized by a Poisson approach and an edge-preserving step through an optimization formulation. This iterative algorithm is easy to implement. Various tests are presented to demonstrate that our method is robust to point noise, normal noise and data holes, and thus produces good surface reconstruction results.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Huibin Li, Yibao Li, Ruixuan Yu, Jian Sun, Junseok Kim,