Intelligent 3D modelling using evolution principle
As technologies advance over the years, machine learning techniques have advanced and are applied in different fields of application. Machine learning techniques are used to help perform the more intricate human task in a shorter amount of time. However, the time required to create a single new thre...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/137989 |
| _version_ | 1811690105058885632 |
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| author | Ong, Wayne Chan Chi |
| author2 | Zheng Jianmin |
| author_facet | Zheng Jianmin Ong, Wayne Chan Chi |
| author_sort | Ong, Wayne Chan Chi |
| collection | NTU |
| description | As technologies advance over the years, machine learning techniques have advanced and are applied in different fields of application. Machine learning techniques are used to help perform the more intricate human task in a shorter amount of time. However, the time required to create a single new three-dimensional (3D) model that is creative is not proportional to the time and effort required by humans to perform the task.
The purpose of this research is to investigate the effectiveness of using machine learning techniques in generating a new set of 3D models that are creative and of a wide variety within a shorter time frame. This is achieved by applying machine learning techniques to learn the feature representation of the components of a 3D model. Using BézierGAN and 3D VoxelGAN, the feature representation of the main body components and decoration components, respectively, from the 3D model is to be learned. The experiment analysed the effectiveness of the low dimensional latent representation in representing the data feed into the model during the training phase. Deformation will be performed on the component to ensure that components can be combined for the generation of new models. Results from the experiments show that the respective BézierGAN and 3D VoxelGAN have achieved the desired output after training was performed on the model. The feature representation of the individual data is captured in the latent representation obtained from the trained model.
Therefore, this research definitively answers the question concerning the effectiveness of the application of machine learning techniques to generate new 3D models that are creative in a shorter period. |
| first_indexed | 2024-10-01T05:58:42Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/137989 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:58:42Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1379892020-04-21T07:06:04Z Intelligent 3D modelling using evolution principle Ong, Wayne Chan Chi Zheng Jianmin School of Computer Science and Engineering asjmzheng@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Computer graphics As technologies advance over the years, machine learning techniques have advanced and are applied in different fields of application. Machine learning techniques are used to help perform the more intricate human task in a shorter amount of time. However, the time required to create a single new three-dimensional (3D) model that is creative is not proportional to the time and effort required by humans to perform the task. The purpose of this research is to investigate the effectiveness of using machine learning techniques in generating a new set of 3D models that are creative and of a wide variety within a shorter time frame. This is achieved by applying machine learning techniques to learn the feature representation of the components of a 3D model. Using BézierGAN and 3D VoxelGAN, the feature representation of the main body components and decoration components, respectively, from the 3D model is to be learned. The experiment analysed the effectiveness of the low dimensional latent representation in representing the data feed into the model during the training phase. Deformation will be performed on the component to ensure that components can be combined for the generation of new models. Results from the experiments show that the respective BézierGAN and 3D VoxelGAN have achieved the desired output after training was performed on the model. The feature representation of the individual data is captured in the latent representation obtained from the trained model. Therefore, this research definitively answers the question concerning the effectiveness of the application of machine learning techniques to generate new 3D models that are creative in a shorter period. Bachelor of Engineering (Computer Science) 2020-04-21T07:06:04Z 2020-04-21T07:06:04Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137989 en SCSE19-0367 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Computer graphics Ong, Wayne Chan Chi Intelligent 3D modelling using evolution principle |
| title | Intelligent 3D modelling using evolution principle |
| title_full | Intelligent 3D modelling using evolution principle |
| title_fullStr | Intelligent 3D modelling using evolution principle |
| title_full_unstemmed | Intelligent 3D modelling using evolution principle |
| title_short | Intelligent 3D modelling using evolution principle |
| title_sort | intelligent 3d modelling using evolution principle |
| topic | Engineering::Computer science and engineering::Computing methodologies::Computer graphics |
| url | https://hdl.handle.net/10356/137989 |
| work_keys_str_mv | AT ongwaynechanchi intelligent3dmodellingusingevolutionprinciple |