Hash Encoding and Brightness Correction in 3D Industrial and Environmental Reconstruction of Tidal Flat Neural Radiation

Abstract

We present an innovative approach to mitigating brightness variations in the unmanned aerial vehicle (UAV)-based 3D reconstruction of tidal flat environments, emphasizing industrial applications. Our work focuses on enhancing the accuracy and efficiency of neural radiance fields (NeRF) for 3D scene synthesis. We introduce a novel luminance correction technique to address challenging illumination conditions, employing a convolutional neural network (CNN) for image enhancement in cases of overexposure and underexposure. Additionally, we propose a hash encoding method to optimize the spatial position encoding efficiency of NeRF. The efficacy of our method is validated using diverse datasets, including a custom tidal flat dataset and the Mip-NeRF 360 dataset, demonstrating superior performance across various lighting scenarios.