Single View Seafloor Recovery from Imaging Sonar via Differentiable Rendering
This work addresses the challenge of single-view 3D recovery from sonar for underwater terrain mapping, offering a training-free alternative that adapts across environments without requiring extensive data.
The paper presents a training-free method for recovering seafloor bathymetry from a single forward-looking imaging sonar image using differentiable rendering, achieving recovery in under 30 seconds. On synthetic data, it outperforms a supervised CNN under distribution shift and remains competitive on rough terrain.
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.