# Neural Radiation Fields in a Tidal Flat Environment
## 4 Discussion  
In regard to the \"ground textures\" dataset presented in Figure 8, it is evident that there are excessively bright highlights on the rocks in the ground texture. We conducted further investigation into this phenomenon. Upon analyzing the image data of the tidal flat scene, we observed that the area in question constitutes a protruding section of the ground, surrounded by bright water bodies. We hypothesize that this result is influenced by the brightness of neighboring submodules during joint optimization, leading to an enhanced representation of brightness in this specific submodule. To mitigate this issue and enhance our reconstruction quality, we intend to incorporate a weight parameter in our future work. This parameter will help control the impact of neighboring submodules on our module, particularly in specific cases. In Table 1, our method exhibits suboptimal performance in terms of LPIPS values within the \"deep water\" dataset. The primary challenge in capturing glossy surfaces lies in the generation of sporadic glossy artifacts that intermittently appear and vanish between rendered views, rather than smoothly traversing surfaces in a physically plausible manner. We hypothesize that factors such as illumination, reflection, perspective, and transparency within the deep-water region significantly influence image generation. These factors can lead to a more intricate and irregular radiation field in the deep-water region, potentially necessitating an increased number of sampling points and greater network capacity for accurate representation. It is worth noting that methods employing remotely operated vehicles (ROVs) or autonomous underwater vehicles (AUVs) are better equipped to capture information in deep water areas [32]. However, adopting such methods inevitably escalates data acquisition costs and may compromise result reliability due to potential data quality issues. In future research, we intend to address this limitation by utilizing the reflection of the observation vector on the local normal vector as input, as opposed to using the observation vector itself. Alternatively, we may introduce a function that characterizes the outgoing radiation within our model for materials with varying degrees of roughness. This approach aims to distinguish between diffuse reflection and specular reflection in smooth materials, further enhancing the expressiveness of our model. Figure 10: Comparison of the results of tidal flats under varying lighting conditions. Overall, in order to facilitate the application of neural radiance fields for 3D reconstruction in tidal flat environments, we introduced an embedded vector for global optimization. This vector takes into account specific crucial lighting conditions unique to tidal flats. To incorporate information from distant regions of the tidal flats, we employed an inversion transformation that effectively transfers external scenery onto the inner surface of a sphere, thereby enhancing the algorithm's representational capabilities. In the context of tidal flat 3D reconstruction, we employ two crucial parameters, PSNR and LPIPS, to validate the superiority of our model over the conventional Nerf model. When compared with the traditional Nerf model, our approach demonstrates an average increase of 2.28 in PSNR and a corresponding average decrease of 0.11 in LPIPS. Our method optimizes the utilization of images acquired through drone-based surveys, resulting in a significantly enhanced capacity for capturing the intricacies of the tidal flat environment. This improvement empowers researchers to conduct more comprehensive assessments of tidal flat ecosystems. Furthermore, it offers invaluable support for a diverse range of scientific investigations, environmental preservation initiatives, and sustainable development objectives. This pioneering approach introduces a fresh perspective to the realm of 3D reconstruction in natural environments by leveraging neural radiance fields.