ShanghaiTech University Knowledge Management System
| Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis | |
| 2024-12-04 | |
| 状态 | 已发表 |
| 摘要 | This paper presents a novel approach for cross-view synthesis aimed at generating plausible ground-level images from corresponding satellite imagery or vice versa. We refer to these tasks as satellite-to-ground (Sat2Grd) and ground-to-satellite (Grd2Sat) synthesis, respectively. Unlike previous works that typically focus on one-to-one generation, producing a single output image from a single input image, our approach acknowledges the inherent one-to-many nature of the problem. This recognition stems from the challenges posed by differences in illumination, weather conditions, and occlusions between the two views. To effectively model this uncertainty, we leverage recent advancements in diffusion models. Specifically, we exploit random Gaussian noise to represent the diverse possibilities learnt from the target view data. We introduce a Geometry-guided Cross-view Condition (GCC) strategy to establish explicit geometric correspondences between satellite and street-view features. This enables us to resolve the geometry ambiguity introduced by camera pose between image pairs, boosting the performance of cross-view image synthesis. Through extensive quantitative and qualitative analyses on three benchmark cross-view datasets, we demonstrate the superiority of our proposed geometry-guided cross-view condition over baseline methods, including recent state-of-the-art approaches in cross-view image synthesis. Our method generates images of higher quality, fidelity, and diversity than other state-of-the-art approaches. |
| 语种 | 英语 |
| DOI | arXiv:2412.03315 |
| 相关网址 | 查看原文 |
| 出处 | Arxiv |
| 收录类别 | PPRN.PPRN |
| WOS记录号 | PPRN:119699074 |
| WOS类目 | Computer Science, Software Engineering |
| 文献类型 | 预印本 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/471018 |
| 专题 | 信息科学与技术学院_PI研究组_师玉娇组 |
| 通讯作者 | Lin, Tao Jun |
| 作者单位 | 1.Australian Natl Univ, Canberra, Australia 2.Univ Surrey, Guildford, England 3.ShanghaiTech Univ, Shanghai, England 4.Ford Motor Co, Dearborn, MI, USA |
| 推荐引用方式 GB/T 7714 | Lin, Tao Jun,Wang, Wenqing,Shi, Yujiao,et al. Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis. 2024. |
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