| Title |
Adaptive Study of Convolutional Neural Networks Based on Transfer Learning in Mechanical Intelligent Fault Diagnosis |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.3.339 |
| Keywords |
Transfer learning; Convolutional neural networks; Fault diagnosis; Boundary self balancing adversarial generation network; Denoising autoencoder |
| Abstract |
This survey paper explores recent advancements in neural rendering, focusing on the development and impact of Neural Radiance Fields (NeRF). Initially, 3D reconstruction relied on methods such as Photogrammetry, Structure from Motion (SfM), and Image-Based Rendering (IBR). While these techniques provided foundational approaches for creating 3D models from 2D images, they were limited in resolution, texture fidelity, and computational efficiency. IBR, in particular, was crucial in producing photorealistic environments using actual photographs but faced challenges in terms of flexibility and rendering complex scenes. NeRF emerged as a novel solution, utilizing neural networks to render 3D scenes from 2D images with improved realism and efficiency. This paper examines four key areas of advancement in the post-NeRF era: 1) Training and Rendering Speed: Enhancements in the efficiency of neural rendering algorithms; 2) Quality: Improvements in image realism and texture representation. 3) 3D Geometry/Reconstruction: Advancements in achieving accurate and detailed 3D models; and 4) Neural Scene Editing: Innovations enabling dynamic modifications in neural-rendered scenes. |