Optimized 3D reconstruction in nearshore underwater environments: A cost-effective pre-processing strategy for Neural Radiance Fields implementation

Abstract

Nearshore oceans, teeming with diverse benthic ecosystems, continue to be a focal point for marine research. While 2D visual representations have been the mainstay in this area, the intricate, multi-dimensional nature of the seafloor ecosystems underscores the need for 3D modeling to capture their full essence. This study unveils a novel approach tailored for static image processing and 3D modeling through Neural Radiance Fields (NeRF), with a particular emphasis on the refined instantNGP variant. A meticulously crafted pipeline is employed, centered on neutralizing the visual impediments brought about by the interplay of light and seawater in underwater imaging. This refined pre-processing strategy ensures that images are primed for a seamless transition to NeRF-based 3D reconstruction, all the while conserving computational resources. The refined image processing techniques rectify underwater color discrepancies, notably the prevalent blue-green hue resulting from unique lighting conditions. Moreover, the system's ability to identify and excise seawater boundaries guarantees that the 3D models remain singularly focused on the richness of the seafloor ecosystems. Remarkably, achieving this does not demand vast datasets or exorbitant computational prowess, positioning it as an ideal fit for processing images from nearshore regions. As a more resource-friendly and efficient counterpart to existing methodologies, this study furnishes marine ecologists with a powerful instrument for RGB-centric 3D renderings of nearshore terrains. Nonetheless, for broader applicability in diverse marine settings, fusing this approach with neural networks could prove invaluable.