Forward-looking sonar (FLS) can capture high-resolution acoustical images from the underwater scenes, maintaining performance even in turbid water conditions and poor lighting. Although neural fields have become popular for 3-D reconstruction from acoustical images, they still suffer from poor accuracy and slow computation. We thus propose a novel framework, namely, neural fields for FLS, which leverages a combination of advanced encoding strategies and tailored loss functions for rapid and accurate object-level 3…