Volumetric shape contexts for mesh co-segmentation

Xie, Xuanmeng; Feng, Jieqing

doi:10.1016/j.cagd.2016.02.006

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…However, when the size of the training dataset is small, or the training meshes are quite complex, the segmentation performance may be significantly affected. The unsupervised co‐segmentation approach does not use a set of labelled training segmented meshes, so its performance usually is lower than the one of supervised co‐segmentation approach [XF16]. Finally, the semi‐supervised co‐segmentation approach popped in as a third alternative for co‐segmentation [LCHB12], which combines the supervised and unsupervised approaches since it learns not only from labelled meshes but also from unlabelled models [XF16].…”

Section: Co‐segmentationmentioning

confidence: 99%

Part‐Based Mesh Segmentation: A Survey

Rodrigues

Morgado

Gomes

2018

Computer Graphics Forum

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This paper surveys mesh segmentation techniques and algorithms, with a focus on part‐based segmentation, that is, segmentation that divides a mesh (featuring a 3D object) into meaningful parts. Part‐based segmentation applies to a single object and also to a family of objects (i.e. co‐segmentation). However, we shall not address here chart‐based segmentation, though some mesh co‐segmentation methods employ such chart‐based segmentation in the initial step of their pipeline. Finally, the taxonomy proposed in this paper is new in the sense that one classifies each segmentation algorithm regarding the dimension (i.e. 1D, 2D and 3D) of the representation of object parts. The leading idea behind this survey is to identify the properties and limitations of the state‐of‐the‐art algorithms to shed light on the challenges for future work.

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Section: Co‐segmentationmentioning

confidence: 99%

Part‐Based Mesh Segmentation: A Survey

Rodrigues

Morgado

Gomes

2018

Computer Graphics Forum

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“…In recent years, the research on cosegmentation [24][25][26] has gradually become the hotspot of model segmentation. But in this paper, we do not relate to the content of this research.…”

Section: Shape Segmentationmentioning

confidence: 99%

Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

Yang

Hao

2018

Mathematical Problems in Engineering

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A novel approach of 3D human model segmentation is proposed, which is based on heat kernel signature and geodesic distance. Through calculating the heat kernel signature of the point clouds of human body model, the local maxima of thermal energy distribution of the model is found, and the set of feature points of the model is obtained. Heat kernel signature has affine invariability which can be used to extract the correct feature points of the human model in different postures. We adopt the method of geodesic distance to realize the hierarchical segmentation of human model after obtaining the semantic feature points of human model. The experimental results show that the method can overcome the defect of geodesic distance feature extraction. The human body models with different postures can be obtained with the model segmentation results of human semantic characteristics.

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“…Feature space analysis has, all the time, attracted broad attention, due to its fundamental role in assisting graphical tasks, such as shape understanding, recognition, decomposition, etc. [1][2][3]. Undoubtedly, effective tools or techniques for analyzing the feature space are always demanding, especially part-aware or meaning bases.…”

Section: Introductionmentioning

confidence: 99%

SAC-NMF-Driven Graphical Feature Analysis and Applications

Wang

et al. 2020

MAKE

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Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by introducing modified Non-negative Matrix Factorization (NMF) model into the graphical feature space and push forward further applications. By analyzing and utilizing the intrinsic ideas behind NMF, we propose conducting the factorization on feature matrices constructed based on descriptors or graphs, which provides a simple but effective way to raise compressed and scale-aware descriptors. In order to enable part-aware model analysis, we modify the NMF model to be sparse and constrained regarding to both bases and encodings, which gives rise to Sparse and Constrained Non-negative Matrix Factorization (SAC-NMF). Subsequently, by adapting the analytical components (including hidden variables, bases, and encodings) to design descriptors, several applications have been easily but effectively realized. The extensive experimental results demonstrate that the proposed framework has many attractive advantages, such as being efficient, extendable, and so forth.

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Volumetric shape contexts for mesh co-segmentation

Cited by 4 publications

References 36 publications

Part‐Based Mesh Segmentation: A Survey

Part‐Based Mesh Segmentation: A Survey

Semantic Segmentation of Human Model Using Heat Kernel and Geodesic Distance

SAC-NMF-Driven Graphical Feature Analysis and Applications

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