TranSR-Ne RF:Super-resolution neural radiance field for reconstruction and rendering of weak and repetitive texture of aviation damaged functional surface  

作　　者：Qichun HU Haojun XU Xiaolong WEI Yizhen YIN Weifeng HE Xinmin HAN Caizhi LI 

机构地区：[1]National Key Lab of Aerospace Power System and Plasma Technology,Air Force Engineering University,Xi’an 710038,China [2]iHarbour Academy of Frontier Equipment Institute of Aero-Engine,Xi’an Jiaotong University,Xi’an 710049,China

出　　处：《Chinese Journal of Aeronautics》2024年第11期447-461,共15页中国航空学报（英文版）

基　　金：supported by the National Science and Technology Major Project,China(No.J2019-Ⅲ-0009-0053);the National Natural Science Foundation of China(No.12075319)。

摘　　要：In order to reconstruct and render the weak and repetitive texture of the damaged functional surface of aviation,an improved neural radiance field,named TranSR-NeRF,is proposed.In this paper,a data acquisition system was designed and built.The acquired images generated initial point clouds through TransMVSNet.Meanwhile,after extracting features from the images through the improved SE-ConvNeXt network,the extracted features were aligned and fused with the initial point cloud to generate high-quality neural point cloud.After ray-tracing and sampling of the neural point cloud,the ResMLP neural network designed in this paper was used to regress the volume density and radiance under a given viewing angle,which introduced spatial coordinate and relative positional encoding.The reconstruction and rendering of arbitrary-scale super-resolution of damaged functional surface is realized.In this paper,the influence of illumination conditions and background environment on the model performance is also studied through experiments,and the comparison and ablation experiments for the improved methods proposed in this paper is conducted.The experimental results show that the improved model has good effect.Finally,the application experiment of object detection task is carried out,and the experimental results show that the model has good practicability.

关 键 词：Functional surface Multi-view reconstruction Neural rendering TranSR-NeRF Image super-resolution Deep learning 

分 类 号：V214[航空宇航科学与技术—航空宇航推进理论与工程]