用统一模型生成可扩展的水下3D环境，解决数据缺失与场景不连贯问题。
BenthicFlow: Generating Extensible Underwater Environments via Flow Matching
- 基于条件流匹配模型，联合生成纹理与深度图。
- 跨区域生成的场景与真实分布高度一致，覆盖大尺度水域。
- 适合水下机器人、海洋测绘等需要逼真3D环境的研究者使用。
水下三维场景理解因高质量3D数据稀缺及表层训练模型难以泛化至水下环境而面临挑战。现有方法通过后期拼接独立生成的图像块构建大场景，但仅在单一调查站点内展现异质地貌。本文提出BenthicFlow，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与深度图。采用受MultiDiffusion启发的采样策略，在生成过程中协调重叠区域，实现无需额外拼接模型的空间可扩展RGBD拼接。生成的拼接图进一步通过表面对齐的高斯面元构建为显式3D底栖环境。在多个地理上分离的调查站点实验表明，BenthicFlow能保持站点特异性外观，并生成与目标分布高度匹配的连贯大尺度3D场景。代码与训练模型已公开于https://github.com/jacomof/BenthicFlow。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。