BenthicFlow: Generating Extensible Underwater Environments via Flow Matching

2026-08-24

AI Summary

本文针对水下环境3D场景理解数据不足问题，提出BenthicFlow框架，通过单一流匹配模型生成对齐的纹理和深度图，构建连贯的大规模3D场景。

Abstract

Computer vision applications for 3D scene understanding in underwater environments remain challenging due to the lack of high-quality 3D data and the inability of surface-trained models to generalize to underwater scenes. To address this challenge, an emerging trend is to employ generative models to close the data domain gap. However, existing methods assemble large scenes by stitching independently generated tiles post hoc with separately trained models, while demonstrating heterogeneous landscapes only within individual survey sites. We introduce BenthicFlow, a unified framework based on a single conditional flow-matching model that jointly generates aligned textures and depth maps. A MultiDiffusion-inspired sampling procedure reconciles overlapping windows throughout the generative trajectory, enabling spatially extensible RGBD mosaics without a separate stitching model. The generated mosaics are subsequently lifted into explicit 3D benthic environments using surface-aligned Gaussian surfels. Experiments across geographically distinct survey sites demonstrate that BenthicFlow preserves site-specific appearance while generating coherent, large-scale 3D scenes that closely match the target distributions. Code and trained models are available at https://github.com/jacomof/BenthicFlow.

Problem

• underwater environments
• 3D scene understanding
• data domain gap
• generative models

Innovation

• conditional flow-matching
• spatially extensible RGBD mosaics
• Gaussian surfels