发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BenthicFlow是基于条件流匹配的统一框架，可联合生成对齐纹理与深度图，实现无需单独拼接模型的可扩展水下RGBD场景，实验证实其能生成匹配目标分布的大型3D海底环境。
AI 中文摘要
由于缺乏高质量的3D数据，以及在地表训练的模型无法泛化到水下场景，面向水下环境3D场景理解的计算机视觉应用仍然面临挑战。为应对这一挑战，新兴趋势是采用生成模型缩小数据域差距。然而，现有方法通过使用单独训练的模型事后拼接独立生成的图块来组装大型场景，仅在单个勘测位点内展示异质景观。我们提出BenthicFlow，这是一个基于单个条件流匹配模型的统一框架，可联合生成对齐的纹理和深度图。一种受MultiDiffusion启发的采样流程，在生成轨迹中协调重叠窗口，无需单独的拼接模型即可实现空间可扩展的RGBD镶嵌图。随后，这些镶嵌图使用地表对齐的高斯表面元被提升为显式的3D海底环境。在地理上不同的勘测位点开展的实验表明，BenthicFlow在保留位点特定外观的同时，生成与目标分布高度匹配的连贯大型3D场景。代码和训练模型可在该https URL获取。
英文摘要
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.
CommentsAccepted to ECCV 2026 in the 2nd Workshop on Marine Vision
