Dense 3D reconstruction using forward-looking sonar (FLS) is essential for ocean exploration. Recent advancements in FLS-based 3D reconstruction using neural radiance fields have emerged, demonstrating promising results. However, their excessively slow reconstruction speed significantly impacts their application in real-world scenarios, primarily due to two reasons: (1) the reliance on MLPs for scene representation leads to slow training, often requiring several hours for reconstruction; and (2) the uniform sampling strategy along the elevation arc is inefficient, greatly hindering both training speed and reconstruction quality. To address these challenges, we propose a voxel-based efficient neural implicit surface reconstruction approach using FLS, featuring three key innovations: 1) Replacing MLPs with voxel grids for scene representation, utilizing a signed distance function (SDF) voxel grid to model geometry and a feature voxel grid to capture appearance. 2) Introducing a hierarchical sampling strategy along the elevation arc to improve sampling efficiency. 3) Applying SDF Gaussian convolution to the SDF voxel grid, effectively reducing noise and surface roughness. Extensive experiments demonstrate that our method significantly outperforms existing unsupervised dense FLS reconstruction techniques. Notably, our approach achieves the same reconstruction quality in just 10 minutes of training that previously required 4 hours with state-of-the-art methods, while also delivering superior results. We will open-source our code upon paper acceptance.
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