# Point Cloud Skeletons via Laplacian-Based Contraction
(Automatically exported from code.google.com/p/skeletonization)
## Bibtex
@inproceedings{cao_smi10,
author = {Junjie Cao and Andrea Tagliasacchi and Matt Olson and Hao Zhang and Zhixun Su},
title = {Point Cloud Skeletons via Laplacian-Based Contraction},
booktitle = {Proc. of IEEE Conf. on Shape Modeling and Applications},
year = 2015}
## Abstract
We present an algorithm for curve skeleton extraction via Laplacian-based contraction. Our algorithm can be applied to surfaces with boundaries, polygon soups, and point clouds. We develop a contraction operation that is designed to work on generalized discrete geometry data, particularly point clouds, via local Delaunay triangulation and topological thinning. Our approach is robust to noise and can handle moderate amounts of missing data, allowing skeleton-based manipulation of point clouds without explicit surface reconstruction. By avoiding explicit reconstruction, we are able to perform skeleton-driven topology repair of acquired point clouds in the presence of large amounts of missing data. In such cases, automatic surface reconstruction schemes tend to produce incorrect surface topology. We show that the curve skeletons we extract provide an intuitive and easy-to-manipulate structure for effective topology modification, leading to more faithful surface reconstruction.
## Compile and execution
The code should be able to run without any setup. Just run eg_skeleton_laplacian_rosa.m and you will find all info needed. More details about the code can be found in https://github.com/ataiya/cloudcontr/blob/master/matlab/readme.txt.
## Data
Geometry models used in the paper are obtained from the AIM@SHAPE shape repository, the Stanford 3D Scanning repository, Lior Shapira, the Princeton Shape Benchmark, Hugues Hoppe, the Digital Plant Laboratory, and our own laser scanning using a Polhemus Cobra FastScan System. All the data used in our paper are saved in https://github.com/ataiya/cloudcontr/tree/master/data.
## Downloads
- [Paper PDF (7.4MB)](https://github.com/ataiya/skeletonization/raw/master/Downloads/cloudcontr_smi10.pdf)
- [Presentation PPT (7.0MB)](https://github.com/ataiya/skeletonization/raw/master/Downloads/SkelContr_SMI10.pptx)
- [Zipped Source Code](https://github.com/ataiya/skeletonization/raw/master/Downloads/cloudcontr_2.0.1.zip) and [KDTree Library](https://github.com/ataiya/skeletonization/raw/master/Downloads/kdtree.rar)
## Teaser of results
<img src="/Downloads/teaser.png" width="30%"/>
<img src="/Downloads/tree_small.jpg" width="30%"/>
<img src="/Downloads/inner eye corners.png" width="30%"/>
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matlab骨架提取源代码-cloudcontr:自动从code.google.com/p/skeletonization导出
共132个文件
m:50个
off:48个
txt:7个
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2021-05-23
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matlab骨架提取源代码通过基于拉普拉斯算子的收缩的点云骨架 (自动从code.google.com/p/skeletonization导出) 比比克斯 @inproceedings{cao_smi10, author = {Junjie Cao and Andrea Tagliasacchi and Matt Olson and Hao Zhang and Zhixun Su}, title = {Point Cloud Skeletons via Laplacian-Based Contraction}, booktitle = {Proc. of IEEE Conf. on Shape Modeling and Applications}, year = 2015} 抽象的 我们提出了一种通过基于拉普拉斯算式的收缩提取曲线骨架的算法。 我们的算法可以应用于具有边界,多边形汤和点云的曲面。 我们开发了一种收缩操作,旨在通过局部Delaunay三角剖分和拓扑稀化来处理广义离散几何数据,尤其是点云。 我们的方法对噪声具有鲁棒性,并且可以处理少量的丢失数据,从而允许对点云进行基于骨架的
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matlab骨架提取源代码-cloudcontr:自动从code.google.com/p/skeletonization导出 (132个子文件)
tree_small.jpg 96KB
LICENSE 11KB
view3d.m 13KB
edge_collapse_update.m 8KB
contraction_by_mesh_laplacian.m 8KB
compute_mesh_weight.m 6KB
pcloud_view.m 6KB
compute_point_weight.m 5KB
GS.m 5KB
edge_collapse.m 4KB
eg_refine_skeleton.m 4KB
erro_assemble.m 4KB
compute_point_point_ring.m 4KB
farthest_sampling_by_sphere_update.m 3KB
drawPlane3d.m 3KB
remove_inner_nodes.m 3KB
build_hierarchy.m 3KB
connect_by_inherit_neigh.m 3KB
eg_skeleton_laplacian_rosa.m 2KB
farthest_sampling_by_sphere.m 2KB
intersectLinePlane.m 2KB
remove_small_cycles.m 2KB
remove_irrelevant_extrama.m 2KB
rosa_lineextract.m 2KB
read_mesh.m 2KB
linePosition3d.m 2KB
read_off.m 2KB
merge_nearby_joints.m 1KB
compute_mesh_laplacian.m 1KB
kdtree_ball_query.m 1KB
kdtree_k_nearest_neighbors.m 1KB
compute_point_laplacian.m 1KB
test_refine_skeleton.m 1KB
test_edge_collapse_update.m 1KB
test_edge_collapse.m 1KB
plot_connectivity.m 1017B
test_connect_by_inherit_neigh.m 1009B
find_joints.m 969B
kdtree_build.m 850B
build_graph.m 820B
test_farthest_sampling_by_sphere.m 702B
find_root_node.m 670B
test_compute_point_point_ring.m 652B
test_remove_small_cycles.m 588B
test_find_root_node.m 557B
write_xyz.m 537B
test_find_joints.m 481B
getoptions.m 476B
kdtree_delete.m 462B
plot_skeleton.m 416B
plot_embedded_graph.m 416B
findcycles.m 407B
simplejoint_v4770_contract_t(4)_nn(30)_WL(9.621282)_WH(1.000000)_sl(3.000000)_skeleton.mat 310KB
README.md 3KB
kdtree_build.mexw32 44KB
kdtree_k_nearest_neighbors.mexw32 36KB
kdtree_ball_query.mexw32 28KB
kdtree_delete.mexw32 24KB
kdtree_build.mexw64 37KB
kdtree_k_nearest_neighbors.mexw64 23KB
kdtree_ball_query.mexw64 15KB
kdtree_delete.mexw64 8KB
FE_final.off 6.61MB
tree_small.off 2.67MB
corn_26441.off 1.52MB
fertilty.off 1.49MB
camel5n.off 1.4MB
camel3n.off 942KB
woman.off 655KB
woman_11690.off 650KB
indolady5_v11125.off 634KB
corn_v12108.off 621KB
eagle_v9928.off 586KB
horse1_0.1.off 576KB
horse1_0.05.off 576KB
horse1_0.25.off 576KB
horse1_0.5.off 576KB
horse1_1.off 575KB
corn_leaf.off 563KB
9HandleTorus.off 548KB
fertility_6n.off 546KB
runman1s1.off 542KB
fertility_4n.off 529KB
horse1.off 519KB
fertility_nonuniform_16239.off 505KB
fertility_nonuniform_8862.off 496KB
result_xyz_0.005.off 466KB
result_xyz_0.01.off 465KB
result_xyz_0.02.off 465KB
result_xyz_0.015.off 465KB
tobacco_v8098.off 455KB
fertility_2n.off 404KB
simplejoint_v4770.off 284KB
fertility_nonuniform_8862.off 275KB
fertility_base_4832.off 270KB
Dinopet_0.25.off 238KB
Dinopet_0.5.off 238KB
Dinopet_1.0.off 237KB
Dinopet_gaussian_0.001000.off 233KB
Dinopet_random_0.010000.off 233KB
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