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http://hdl.handle.net/10397/90947
Title: | Constructing an efficient and adaptive learning model for 3D object generation | Authors: | Hu, J Deng, W Liu, Q Lam, KM Lou, P |
Issue Date: | Jun-2021 | Source: | IET image processing, June 2021, v. 15, no. 8, p. 1745-1758 | Abstract: | Studying representation learning and generative modelling has been at the core of the 3D learning domain. By leveraging the generative adversarial networks and convolutional neural networks for point-cloud representations, we propose a novel framework, which can directly generate 3D objects represented by point clouds. The novelties of the proposed method are threefold. First, the generative adversarial networks are applied to 3D object generation in the point-cloud space, where the model learns object representation from point clouds independently. In this work, we propose a 3D spatial transformer network, and integrate it into a generation model, whose ability for extracting and reconstructing features for 3D objects can be improved. Second, a point-wise approach is developed to reduce the computational complexity of the proposed network. Third, an evaluation system is proposed to measure the performance of our model by employing various categories and methods, and the error, considered as the difference between synthesized objects and raw objects are quantitatively compared, is less than 2.8%. Extensive experiments on benchmark dataset show that this method has a strong ability to generate 3D objects in the point-cloud space, and the synthesized objects have slight differences with man-made 3D objects. | Publisher: | Institution of Engineering and Technology | Journal: | IET image processing | ISSN: | 1751-9659 | EISSN: | 1751-9667 | DOI: | 10.1049/ipr2.12146 | Rights: | © 2021 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology This is an open access article under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. The following publication Hu, J., Deng, W., Liu, Q., Lam, K. M., & Lou, P. (2021). Constructing an efficient and adaptive learning model for 3D object generation. IET Image Processing, 15(8), 1745-1758 is available at https://doi.org/10.1049/ipr2.12146 |
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