# Research on Seafloor 3D Reconstruction Method Based on Sparse Measurement Points

## TL;DR

This paper introduces a new method for reconstructing seafloor topography using sparse data, combining fractal and Gaussian process models to improve accuracy and detail.

## Contribution

The novel hybrid model integrates fractal self-similarity and Gaussian processes for enhanced seafloor 3D reconstruction from sparse data.

## Key findings

- The hybrid model reduces average errors by 30–40% compared to traditional methods.
- The method achieves an R2 value of 0.9836, indicating high accuracy in seafloor reconstruction.
- Perlin noise improves the naturalness and detail of the reconstructed terrain.

## Abstract

Seafloor 3D reconstruction is a core technology for seafloor topography and deformation monitoring. Due to the complexity of the deep-sea environment and the high requirements for measurement devices, long-term monitoring can only acquire low-resolution and limited seafloor topography data. This leads to difficulties for existing 3D reconstruction algorithms in handling details and accuracy, especially with complex variations in seafloor terrain, which poses higher demands on 3D reconstruction algorithms. This study proposes a “fractal–Gaussian process” hybrid model, leveraging the fractal self-similarity property to precisely capture complex local details of the seafloor terrain, combined with the Bayesian global optimization ability of the Gaussian process model, to achieve high-resolution modeling of seafloor 3D reconstruction. Finally, Perlin noise is introduced to enhance the naturalness and detail representation of the terrain. Experiments show that under sparse data conditions, the proposed method significantly outperforms traditional interpolation methods, with average errors reduced by 30–40% and an R2 value of 0.9836.