A data-centric unsupervised 3D mesh segmentation method

Download

A_DATA_CENTRIC_UNSUPERVISED_3D_MESH_SEGMENTATION_METHOD_TalyaTumerSivri_openmetu.pdf

Date

2022-12-02

Author

Tümer Sivri, Talya

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

518
views

362
downloads

Modeling, texture mapping, shape compression, simplification, and skeleton extracting are popular and essential topics in mesh segmentation applications. As it serves various purposes in computer science, the mesh segmentation problem is an active and prominent research area. With the help of growing machine learning, deep learning algorithms, and computation power, different methods have been applied to solve the 3D mesh segmentation problem more efficiently. In this thesis, we solve the 3D mesh segmentation problem from a different perspective. We present a novel data-centric AI approach for the segmentation of 3D meshes. We used node2vec, a semi-supervised learning algorithm, to train vector embedding representation for each node in a 3D mesh graph. This method makes the mesh data easier to process and more compact. In addition, we make dimension reduction with this method, which is very important for reducing computation costs and eliminating the curse of dimensionality. In other words, we learn information from nodes and edge connections between nodes. Then, the unsupervised learning algorithm K-Means was used to cluster each node according to node embedding information and two different initialization method was performed. Moreover, our data-centric approach is much lower in computational cost than complex models such as CNN and RNN. Instead of using complex and computationally expensive models, we apply data-centric methods to improve the raw data representation. The main contribution of this study is developing a data-centric AI framework by utilizing a node2vec embedding algorithm, machine learning, and deep learning techniques. Additionally, we adapt the cosine similarity method to compare and evaluate the node embedding vectors trained with different hyperparameters. Also, we developed a new algorithm for choosing the optimal cluster number, calculated with geodesic distance on the 3D mesh. Thus, we provide competitive results compared to the state-of-the-art mesh segmentation methods.

Subject Keywords

3D mesh segmentation, Unsupervised learning, Embedding, node2vec, K-Means, Geodesic distance

URI

https://hdl.handle.net/11511/101185

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

A Partition Based Method for Spectrum-Preserving Mesh Simplification Yazgan, Misranur; Sahillioğlu, Yusuf; Department of Computer Engineering (2022-8-29) When the complexity of a mesh starts introducing high computational costs, mesh simplification methods come into the picture, to reduce the number of elements utilized to represent the mesh. Majority of the simplification methods focus on preserving the appearance of the mesh, ignoring the spectral properties of the differential operators derived from the mesh. The spectrum of the Laplace-Beltrami operator is essential for a large subset of applications in geometry processing. Coarsening a mesh without cons...
A shape deformation algorithm for constrained multidimensional scaling Sahillioğlu, Yusuf (2015-12-01) We present a new Euclidean embedding technique based on volumetric shape registration. Extrinsic representation of the intrinsic geometry of a shape is preferable in various computer graphics applications as it poses only a small degrees of freedom to deal with during processing. A popular Euclidean embedding approach to achieve such a representation is multidimensional scaling (MDS), which, however, distorts the original geometric details drastically. Our method introduces a constraint on the original MDS ...
IMPROVED PREDICTION FOR LAYERED PREDICTIVE ANIMATED MESH COMPRESSION Bici, M. Oguz; Akar, Gözde (2010-09-29) In this paper, we deal with layered predictive compression of animated meshes represented by series of 3D static meshes with same connectivity. We propose two schemes to improve the prediction. First improvement is using weighted spatial prediction rather than averaging neighbor vertices. The second improvement is a novel predictor based on rotation angle of incident triangles in current and previous frames. The experimental results show that around 6- 10 % bitrate reduction can be achieved by replacing the...
A 2-D unsteady Navier-Stokes solution method with overlapping/overset moving grids Tuncer, İsmail Hakkı (1996-01-01) A simple, robust numerical algorithm to localize intergrid boundary points and to interpolate unsteady solution variables across 2-D, overset/overlapping, structured computational grids is presented. Overset/ overlapping grids are allowed to move in time relative to each other. The intergrid boundary points are localized in terms of three grid points on the donor grid by a directional search algorithm. The final parameters of the search algorithm give the interpolation weights at the interpolation point. Th...
Supervised mesh segmentation for 3D objects with graph Convolutional neural networks Perek, Emir Kaan; Sahillioğlu, Yusuf; Department of Computer Engineering (2019) Mesh segmentation is a fundamental application that is primarily used for understanding and analyzing 3D shapes in a broad range of areas in Computer Science. With the increasing trend of deep learning, there have been many learning-based solutions to the mesh segmentation problem based on the classification of the individual mesh polygons. In this thesis, we cast mesh segmentation as a supervised graph labeling problem by using Graph Convolutional Neural Networks (GCNN). We treat a mesh object as a graph t...

Citation Formats

T. Tümer Sivri, “A data-centric unsupervised 3D mesh segmentation method,” M.S. - Master of Science, Middle East Technical University, 2022.