One Model, Two Worlds: Bidirectional Sonar-Optical Translation

2026-09-05

AI Summary

本文提出DARB和ARS方法，通过非对称物理先验路径和自适应现实监督，解决双向声纳-光学图像转换问题，减少了存储与计算资源需求。

Abstract

Translating between imaging sonar and optical cameras is valuable for underwater perception, but supporting both directions with separate models duplicates storage and computation. A unified bidirectional model is therefore attractive, yet existing approaches largely treat the two directions symmetrically despite their fundamentally different image-formation physics. We argue that sharing a generative model does not require sharing the physics. We introduce the Direction-Asymmetric Realism Bridge (DARB), which retains a shared diffusion-bridge trunk while routing direction-specific physical priors through asymmetric pathways: range-aware modulation for sonar-to-optical translation and polar ray-dependent processing for optical-to-sonar translation. We further show that symmetry in training is also costly: applying a common realism schedule reduces sonar-to-optical PSNR by 2.60 dB. Our Adaptive Realism Supervision (ARS) instead determines when, where, and how strongly perceptual supervision is applied from reconstruction quality and gradient balance. Together, DARB and ARS enable one bidirectional model to match the sonar-to-optical specialist within 0.11 dB PSNR, outperform the optical-to-sonar specialist by 0.70 FID, and surpass two independently trained BBDMs on seven of eight metrics.

Problem

• bidirectional translation
• imaging sonar
• optical cameras
• underwater perception
• shared model

Innovation

• Direction-Asymmetric Realism Bridge
• Adaptive Realism Supervision
• bidirectional model
• asymmetric pathways
• perceptual supervision