Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis (2412.03315v1)

Abstract: 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.

• The paper proposes a geometry-guided cross-view diffusion model that handles the one-to-many nature of cross-view image synthesis using explicit geometric correspondences.
• The method combines a Cross-View Geometry Projection module to map geometric relationships with a Latent Diffusion Model framework for synthesis.
• Benchmarking shows state-of-the-art performance on multiple datasets, improving image quality, diversity, and flexibility for both synthesis directions.

Geometry-Guided Cross-View Diffusion for One-to-Many Cross-View Image Synthesis

The paper "Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis" presents a novel approach to tackle the complex task of cross-view image synthesis, specifically generating ground-level images from satellite imagery and vice versa. This task, which the authors refer to as satellite-to-ground (Sat2Grd) and ground-to-satellite (Grd2Sat) synthesis, is complex due to the inherent one-to-many nature of the problem. The challenges arise from differences in illumination, weather conditions, and occlusions between the ground and satellite views.

Unlike traditional methods which adopt a deterministic one-to-one generation approach, this research leverages recent developments in diffusion models to effectively model the uncertainties associated with these variations. The core contribution of this work is the innovative use of a Geometry-guided Cross-view Condition (GCC) strategy, which introduces explicit geometric correspondences between satellite and street-view images to handle geometry ambiguity resulting from camera poses.

Key Contributions and Methodology

The proposed Geometry-guided Cross-view Condition (GCC) bridges the gap between varying viewpoints using a diffusion model framework. The model utilizes random Gaussian noise as a representation of the diverse possibilities learned from the target view data. By applying a Geometry-guided Cross-view Conditioning strategy, the authors establish explicit geometric correspondences between image features from the satellite and ground perspectives, strengthening the synthesis process.

The approach is built upon two major components:

1. Cross-View Geometry Projection (CVGP) Module: This module explicitly maps the geometric relationship between the ground and satellite views using camera pose information. This is accomplished by projecting multi-level image features rather than raw RGB data, ensuring robustness against potential misalignments caused by geometric assumptions.
2. Latent Diffusion Models (LDM) Framework: By training a diffusion model in a learned image latent space, the framework is able to reconstruct target images from Gaussian noise, driven by the GCC.

Experimental Results

The authors conducted extensive experiments across three benchmark datasets: CVUSA, CVACT, and KITTI. Results showed that their method outperformed existing state-of-the-art approaches in both quantitative and qualitative assessments. The approach significantly improved image quality, fidelity, and diversity, as evidenced by favorable SSIM, PSNR, LPIPS, and FID scores.

A notable aspect of the research is the flexible capability of the proposed method to handle both Sat2Grd and Grd2Sat tasks within the same framework. This adaptability is vital given the varying challenges each synthesis direction presents, with the Grd2Sat task being more demanding due to occlusions and the limited field of view in ground imagery.

Implications and Future Directions

This research provides a compelling framework that not only enhances the synthesis quality of cross-view images but also broadens the potential applications in virtual reality, data augmentation, and image matching scenarios. The robust geometry-guided conditioning approach ensures adaptability in various environmental conditions, creating a more generalizable solution.

Looking forward, the integration of other modalities such as text, depth information, or simultaneous training across multiple datasets may further enhance the model's learning capabilities and application breadth. Additionally, exploring ways to mitigate the complexities in Grd2Sat synthesis could also be beneficial.

In summary, this paper introduces a significant advancement in the domain of cross-view image synthesis, offering insights and methodologies that could pave the way for further research and development in the field of computational photography and visual scene understanding.