# Neural Radiation Fields in a Tidal Flat Environment
###### Abstract
Currently, the employment of unmanned systems [2; 3] for the acquisition of environmental data in natural settings is a highly promising trend. Unmanned systems offer several advantages: (1) they possess the capability to encompass vast expanses of tidal flat areas; (2) they facilitate considerable time and manpower savings; (3) they enable non-contact data collection, thereby minimizing environmental disruption and degradation; and (4) they are adept at navigating complex terrains and accessing remote locations. Continuous advancements and research in electric motor technology have played a pivotal role in enhancing the performance, efficiency, and sustainability of unmanned systems [4; 5]. Presently, numerous endeavors involve the utilization of unmanned aerial vehicles to collect remote sensing imagery of natural environments [6; 7], subsequently facilitating the evaluation of ecological well-being. However, relative to merely employing 2D perspectives to detect changes in tidal flat environments, the utilization of image-based 3D reconstruction enables the acquisition of more intricate and comprehensive tidal flat information. Through 3D reconstruction of tidal flats, researchers can obtain detailed and holistic terrain models, encompassing elevation, slope, and topographical characteristics. These models facilitate visualization and simulation of the tidal flats, providing researchers and decision-makers with a more intuitive understanding of the morphological dynamics and enabling more scientifically informed decision-making. Additionally, 3D reconstruction [8; 9] empowers more profound quantitative analysis, such as the measurement of tidal flat volume, surface area, slope, and other parameters, which hold immense value for environmental monitoring and management. The synthesis of novel view images from a collection of 2D pictures has been a long-standing problem in computer vision. The entire process can be described as follows: feature points are detected from the 2D images, and descriptors are added to these feature points [10; 11] that serve as unique signatures for subsequent image matching and correlation. Feature point matching is performed among multiple sets of images, establishing correspondences between points in different images. This process allows for the estimation of each camera's position, orientation, and 3D point triangulation within the scene. Ultimately, camera poses and 3D points undergo a combined optimization process known as bundle adjustment, which aims to minimize the differences between projected points and their corresponding image points. Recent works have seen many classical approaches based on structure-from-motion (SfM) [12] or image feature-based 3D reconstruction [13]. However, neural rendering techniques have made significant breakthroughs in this field, offering new insights for the 3D reconstruction of tidal flat environments. Neural radiance fields [14] (Nerf) employ neural networks to represent the radiance field and density of the reconstructed scene, then utilize volume rendering to reconstruct the target scene. Leveraging deep learning technology, Nerf infers the 3D structure and appearance of the scene from a set of observed images, thereby synthesizing new views from previously unobserved viewpoints. This approach has demonstrated remarkable results in many challenging scenarios, significantly enhancing the quality of 3D reconstruction. While Nerf-based techniques have demonstrated exceptional proficiency in representing intricate geometries and smoothly varying appearances concerning viewpoints, Nerf assumes a constant density and radiance across the world, one which remains valid only under static conditions encompassing geometry, material, and lighting. Consequently, when applying Nerf to the 3D reconstruction of tidal environments, we encountered pronounced artifacts, excessive smoothing, and other pseudo-phenomena in terrain units highly sensitive to lighting variations.