We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-the-art 3D acoustic modeling algorithms is highly dependent on accurate pose estimation; small errors in sensor pose can lead to severe reconstruction artifacts. In this paper, we propose an algorithm that jointly optimizes the neural scene representation and sonar poses. Our algorithm does so by parameterizing the 6DoF poses as learnable parameters and backpropagating gradients through the neural renderer and implicit representation. We validated our algorithm on both real and simulated datasets. It produces high-fidelity 3D reconstructions even under significant pose drift.

翻译：本文研究利用存在位姿漂移的传感器采集的声学图像，优化神经隐式曲面以实现三维重建的问题。当前最先进的声学三维建模算法的精度高度依赖于精确的位姿估计；传感器位姿的微小误差可能导致严重的重建伪影。本文提出一种联合优化神经场景表示与声纳位姿的算法。该算法通过将六自由度位姿参数化为可学习参数，并通过神经渲染器与隐式表示进行梯度反向传播来实现优化。我们在真实与仿真数据集上验证了所提算法。即使在显著的位姿漂移下，该算法仍能生成高保真度的三维重建结果。