仅用一张声呐图30秒内恢复海底高程，无需训练
Single View Seafloor Recovery from Imaging Sonar via Differentiable Rendering
- 通过可微渲染模拟声呐成像过程，优化显式高度场
- 合成数据上优于监督CNN，且在地形变化时保持稳定
- 无需训练数据，适配不同传感器与新环境
由于光衰减和浑浊，声呐是水下高分辨率成像的唯一可行模态。前视成像声呐虽能获取距离和水平角度信息，但垂直结构被压缩为平面图像，导致三维重建困难。声呐常用于海底地形测绘（地形高程），但现有方法需多视角、昂贵多传感器或大量训练数据，限制了其应用与环境适应性。本文提出一种无训练方法，仅凭单张声呐图像，在30秒内通过可微渲染恢复地形高程，前提是已知海底倾角。据我们所知，这是首个针对声呐单视图高程恢复的可微渲染方法。该方法实现可微声呐光线追踪，并优化显式高度场以复现目标图像。在合成数据集上，该方法在分布外情形下优于监督卷积神经网络（CNN），在粗糙地形上表现接近，而CNN在分布内表现更优。通过建模声呐过程的物理先验，本方法可在不依赖训练数据的情况下适应不同传感器配置与环境。
原文摘要 · Abstract (English)
Sonar is often the only modality suitable for high-resolution imaging underwater due to light attenuation and turbidity. Forward-looking imaging sonar provides measurements over range and horizontal angle but collapses vertical structure into a flat image, creating ambiguities that make 3D recovery challenging. A common use case for imaging sonar is underwater terrain mapping (bathymetry), yet current methods require many views, expensive multi-sensor setups, or significant training data, which limits use and adaptability to new environments. We present a training-free method that recovers bathymetry from a single sonar image in under 30 seconds via differentiable rendering, conditioned on a known seafloor tilt. To our knowledge, this is the first differentiable rendering approach for single-view height recovery in sonar. Our method implements differentiable sonar ray tracing and optimizes an explicit height field to reproduce the target image. On synthetic datasets, our approach outperforms a supervised CNN under distribution shift and remains close on rough terrain, while the CNN wins in-distribution. By modeling physically grounded priors of the sonar process, our method adapts across sensor configurations and environments without training data.
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