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.
| Original language | English |
|---|---|
| Pages (from-to) | 108-118 |
| Number of pages | 11 |
| Journal | Computer Vision and Image Understanding |
| Volume | 169 |
| DOIs | |
| Publication status | Published - 2018 Apr 1 |
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Keywords
- fast Fourier transform
- l gradient minimization
- l sparsity
- Point cloud
- Surface reconstruction
ASJC Scopus subject areas
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
Cite this
Surface reconstruction from unorganized points with l0 gradient minimization. / Li, Huibin; Li, Yibao; Yu, Ruixuan; Sun, Jian; Kim, Junseok.
In: Computer Vision and Image Understanding, Vol. 169, 01.04.2018, p. 108-118.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Surface reconstruction from unorganized points with l0 gradient minimization
AU - Li, Huibin
AU - Li, Yibao
AU - Yu, Ruixuan
AU - Sun, Jian
AU - Kim, Junseok
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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.
KW - fast Fourier transform
KW - l gradient minimization
KW - l sparsity
KW - Point cloud
KW - Surface reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85041574617&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85041574617&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2018.01.009
DO - 10.1016/j.cviu.2018.01.009
M3 - Article
AN - SCOPUS:85041574617
VL - 169
SP - 108
EP - 118
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
ER -