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点云稀疏编码三维模型簇协同分割

Co-Segmentation of 3D Model Clusters Based on Point Cloud Sparse Coding

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摘要

为了在函数空间内将多个三维模型进行关联,并在整个模型簇上进行协同分割,提出了一种基于点云稀疏编码的三维模型簇协同分割方法。首先,提取点云数据特征,将三维信息转换至特征空间;其次,用深度学习网络将特征向量分解成基向量,并构建字典矩阵及稀疏向量;最后,对测试数据进行稀疏表示,并确定点云模型中每个点所属的类别,将同类点划分到同一区域以得到协同分割结果。实验结果表明,算法在ShapeNet Parts数据集上的分割准确率达到了85.7%。所构建的协同分割算法能够有效地计算模型簇的关联结构,与当前主流分割算法相比,分割效果和准确率均得到提升。

Abstract

Aiming at the problems of the co-analysis of multiple 3D models in the function space and the co-segmentation of the whole model cluster, we propose a co-segmentation method based on point cloud sparse coding. First, the point cloud feature is extracted and the 3D information is transformed into the feature space. Second, the dictionary matrix and sparse vectors are constructed after the decomposition of the feature vectors into the base vectors by the deep learning network. Finally, the test data is represented by dictionary sparseness and the category of each point in the point cloud model is determined. To get the co-segmentation result, the homogeneous points are divided into the same region. The experimental results show that the segmentation accuracy on ShapeNet Parts dataset obtained using the proposed algorithm is 85.7%. Compared to the current mainstream algorithms used for segmentation, the proposed algorithm can not only compute the relational structure of model clusters more effectively, but also improve the segmentation accuracy and effect.

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补充资料

中图分类号:TP391, 文献标志码 A, doi: 10.3788/LOP57.201510

DOI:10.3788/LOP57.201510

所属栏目:机器视觉

基金项目:国家自然科学基金;

收稿日期:2020-02-02

修改稿日期:2020-03-09

网络出版日期:2020-10-01

作者单位    点击查看

杨军:兰州交通大学电子与信息工程学院, 甘肃 兰州 730070
李东浩:兰州交通大学自动化与电气工程学院, 甘肃 兰州 730070

联系人作者:杨军(yangj@mail.lzjtu.cn)

备注:国家自然科学基金;

【1】Zhang J Y, Zheng J M, Wu C L, et al. Variational mesh decomposition [J]. ACM Transactions on Graphics. 2012, 31(3): 1-14.

【2】Yi L, Su H, Guo X W, et al. SyncSpecCNN: synchronized spectral CNN for 3D shape segmentation . [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE. 2017, 6584-6592.

【3】Li Y, Bu R, Sun M, et al. Pointcnn: convolution on X-transformed points . [C]∥Advances in Neural Information Processing Systems, December 2-8, 2018, Montréal, Canada. New York, USA: NIPS. 2018, 820-830.

【4】Wang P Y, Gan Y, Shui P P, et al. 3D shape segmentation via shape fully convolutional networks [J]. Computers & Graphics. 2018, 70: 128-139.

【5】Baraniuk R G, Candes E, Elad M, et al. Applications of sparse representation and compressive sensing [J]. Proceedings of the IEEE. 2010, 98(6): 906-909.

【6】Xiao D, Lin H W, Xian C H, et al. CAD mesh model segmentation by clustering [J]. Computers & Graphics. 2011, 35(3): 685-691.

【7】Benjamin W, Polk A W. Vishwanathan S V N, et al. Heat walk: robust salient segmentation of non-rigid shapes [J]. Computer Graphics Forum. 2011, 30(7): 2097-2106.

【8】Yan D M, Wang W P, Liu Y, et al. Variational mesh segmentation via quadric surface fitting [J]. Computer-Aided Design. 2012, 44(11): 1072-1082.

【9】Rodrigues R S V, Morgado J F M, Gomes A J P. A contour-based segmentation algorithm for triangle meshes in 3D space [J]. Computers & Graphics. 2015, 49: 24-35.

【10】Shu Z Y, Qi C W, Xin S Q, et al. Unsupervised 3D shape segmentation and co-segmentation via deep learning [J]. Computer Aided Geometric Design. 2016, 43: 39-52.

【11】Wu Z R, Song S R, Khosla A, et al. 3D ShapeNets: a deep representation for volumetric shapes . [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE. 2015, 1912-1920.

【12】Kalogerakis E, Averkiou M, Maji S, et al. 3D shape segmentation with projective convolutional networks . [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE. 2017, 6630-6639.

【13】Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition . [C]∥ International Conference on Learning Representations, May 7-9, 2015, San Diego, CA, USA. New York: IEEE. 2015, 1409-1418.

【14】Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [J]. Communications of the ACM. 2017, 60(6): 84-90.

【15】Huang H B, Kalogerakis E, Chaudhuri S, et al. Learning local shape descriptors from part correspondences with multiview convolutional networks [J]. ACM Transactions on Graphics. 2018, 37(1): 1-14.

【16】Li Y, Tong G F, Yang J C, et al. 3D point cloud scene data acquisition and its key technologies for scene understanding [J]. Laser & Optoelectronics Progress. 2019, 56(4): 040002.
李勇, 佟国峰, 杨景超, 等. 三维点云场景数据获取及其场景理解关键技术综述 [J]. 激光与光电子学进展. 2019, 56(4): 040002.

【17】Tong G F, Du X C. Li Y, et. al. Three-dimensional point cloud classification of large outdoor scenes based on vertical slice sampling and centroid distance histograms [J]. Chinese Journal of Lasers. 2018, 45(10): 1004001.
佟国峰, 杜宪策, 李勇, 等. 基于切片采样和质心距直方图特征的室外大场景三维点云分类 [J]. 中国激光. 2018, 45(10): 1004001.

【18】Charles R Q, Su H, Mo K C, et al. PointNet: deep learning on point sets for 3D classification and segmentation . [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE. 2017, 652-660.

【19】Wang X H, Wu L S, Chen H W, et al. Feature line extraction from a point cloud based on region clustering segmentation [J]. Acta Optica Sinica. 2018, 38(11): 1110001.
王晓辉, 吴禄慎, 陈华伟, 等. 基于区域聚类分割的点云特征线提取 [J]. 光学学报. 2018, 38(11): 1110001.

【20】Golovinskiy A, Funkhouser T. Consistent segmentation of 3D models [J]. Computers & Graphics. 2009, 33(3): 262-269.

【21】Yang J, Tian Z H, Li L J, et al. Segmentation of 3D geometric models based on mesh Laplace [J]. Computer Science. 2015, 42(5): 295-299.
杨军, 田振华, 李龙杰, 等. 基于网格Laplace的三维几何模型分割 [J]. 计算机科学. 2015, 42(5): 295-299.

【22】Wang Y H. Asafi S, van Kaick O, et al. Active co-analysis of a set of shapes [J]. ACM Transactions on Graphics. 2012, 31(6): 1-10.

【23】Sidi O, van Kaick O, Kleiman Y, et al. Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering [J]. ACM Transactions on Graphics. 2011, 30(6): 1-10.

【24】Yang J, Zhang P. Three-dimensional shape segmentation by combining topological persistence and heat diffusion theory [J]. Journal of Image and Graphics. 2018, 23(6): 887-895.
杨军, 张鹏. 结合拓扑持续性和热扩散理论的3维模型分割 [J]. 中国图象图形学报. 2018, 23(6): 887-895.

【25】Muralikrishnan S, Kim V G, Chaudhuri S. Tags2Parts: discovering semantic regions from shape tags . [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York:IEEE. 2018, 2926-2935.

【26】Yi L, Kim V G, Ceylan D, et al. A scalable active framework for region annotation in 3D shape collections [J]. ACM Transactions on Graphics. 2016, 35(6): 1-12.

【27】Qi C R, Yi L, Su H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space . [C]∥Advances in Neural Information Processing Systems, December 4-9, 2017, Long Beach, CA, USA. New York: NIPS. 2017, 5099-5108.

引用该论文

Yang Jun,Li Donghao. Co-Segmentation of 3D Model Clusters Based on Point Cloud Sparse Coding[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201510

杨军,李东浩. 点云稀疏编码三维模型簇协同分割[J]. 激光与光电子学进展, 2020, 57(20): 201510

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