Survey and Evaluation of Neural 3D Shape Classification Approaches

2021 ◽  
pp. 1-1
Author(s):  
Martin Mirbauer ◽  
Miroslav Krabec ◽  
Jaroslav Krivanek ◽  
Elena Sikudova
Keyword(s):  
Shape Classification ◽  
3D Shape ◽  
Survey And Evaluation

scholarly journals Survey and Evaluation of Neural 3D Shape Classification Approaches

2021 ◽  
Author(s):  
Martin Mirbauer ◽  
Miroslav Krabec ◽  
Jaroslav Křivánek ◽  
Elena Šikudová
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
Shape Classification ◽  
3D Shape ◽  
3D Objects ◽  
Multiple Neural Networks ◽  
Survey And Evaluation ◽  
Selection Of

<div> <div> <div> <p>Classification of 3D objects – the selection of a category in which each object belongs – is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on three 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being generally unable to reach the accuracies reported in the original papers, we can compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites. </p> </div> </div> </div>

scholarly journals Survey and Evaluation of Neural 3D Shape Classification Approaches

2021 ◽  
Author(s):  
Martin Mirbauer ◽  
Miroslav Krabec ◽  
Jaroslav Křivánek ◽  
Elena Šikudová
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Deep Neural Networks ◽  
Shape Classification ◽  
3D Shape ◽  
3D Objects ◽  
Multiple Neural Networks ◽  
Survey And Evaluation ◽  
Selection Of

<div> <div> <div> <p>Classification of 3D objects – the selection of a category in which each object belongs – is of great interest in the field of machine learning. Numerous researchers use deep neural networks to address this problem, altering the network architecture and representation of the 3D shape used as an input. To investigate the effectiveness of their approaches, we conduct an extensive survey of existing methods and identify common ideas by which we categorize them into a taxonomy. Second, we evaluate 11 selected classification networks on three 3D object datasets, extending the evaluation to a larger dataset on which most of the selected approaches have not been tested yet. For this, we provide a framework for converting shapes from common 3D mesh formats into formats native to each network, and for training and evaluating different classification approaches on this data. Despite being generally unable to reach the accuracies reported in the original papers, we can compare the relative performance of the approaches as well as their performance when changing datasets as the only variable to provide valuable insights into performance on different kinds of data. We make our code available to simplify running training experiments with multiple neural networks with different prerequisites. </p> </div> </div> </div>

3D-MetaConNet: Meta-learning for 3D Shape Classification and Segmentation

2021 ◽  
Author(s):  
Hao Huang ◽  
Xiang Li ◽  
Lingjing Wang ◽  
Yi Fang
Keyword(s):  
Shape Classification ◽  
3D Shape ◽  
Meta Learning

scholarly journals MeshNet: Mesh Neural Network for 3D Shape Representation

2019 ◽  
Vol 33 ◽  
pp. 8279-8286 ◽  
Author(s):  
Yutong Feng ◽  
Yifan Feng ◽  
Haoxuan You ◽  
Xibin Zhao ◽  
Yue Gao
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Point Cloud ◽  
State Of The Art ◽  
Shape Representation ◽  
Shape Classification ◽  
Retrieval Performance ◽  
3D Shape ◽  
General Architecture ◽  
3D Shapes

Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

3D shape classification with NNLS coding and optimal projections technique

2019 ◽  
Vol 78 (24) ◽  
pp. 34689-34706 ◽  
Author(s):  
F. Fotopoulou ◽  
S. Oikonomou ◽  
G. Economou
Keyword(s):  
Shape Classification ◽  
3D Shape

The Layerizing VoxPoint Annular Convolutional Network for 3D Shape Classification

2020 ◽  
Vol 39 (7) ◽  
pp. 291-300
Author(s):  
Tong Wang ◽  
Wenyuan Tao ◽  
Chung‐Ming Own ◽  
Xiantuo Lou ◽  
Yuehua Zhao
Keyword(s):  
Shape Classification ◽  
Convolutional Network ◽  
3D Shape

scholarly journals Auto-ORVNet: Orientation-Boosted Volumetric Neural Architecture Search for 3D Shape Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 12942-12954
Author(s):  
Zijun Ma ◽  
Ziqin Zhou ◽  
Yan Liu ◽  
Yinjie Lei ◽  
Hua Yan
Keyword(s):  
Shape Classification ◽  
3D Shape ◽  
Neural Architecture
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