Edge-Aware GAN for Translating Synthetic Remote Sensing Images to Realistic Domain while Preserving Stereo Constraints

Core Concepts

A lightweight edge-aware GAN model that performs unpaired image-to-image translation of synthetic remote sensing images to realistic domain while maintaining stereo constraints.

Abstract

The paper proposes an edge-aware GAN-based network, SyntStereo2Real, that effectively tackles the task of translating synthetic remote sensing images to realistic domain while preserving stereo constraints. The key highlights are: The model incorporates edge maps of input images as an additional input to the encoder, which helps retain the structural information during translation and prevents blurred boundaries. A warping loss is included to enforce stereo consistency between the translated left and right images. Extensive experiments on remote sensing and autonomous driving datasets demonstrate that the proposed model outperforms existing methods both quantitatively and qualitatively. The model is lightweight and computationally efficient compared to previous approaches that combine separate networks for image translation and stereo matching. The edge-aware translation and stereo constraint preservation enable better performance of downstream tasks like disparity estimation.

Stats

The model was evaluated on two datasets: SyntCities (synthetic) and Urban Semantic 3D (real) for remote sensing Driving (synthetic) and KITTI2015 (real) for autonomous driving The key metrics used for evaluation are: Median Absolute Deviation (MAD) of disparity estimation 3-pixel accuracy percentage 1-pixel accuracy percentage

Quotes

"Our approach focuses on the specific task of translating synthetic images to realistic domain while maintaining the stereo constraints, which means that pixels do not move within or to another epipolar line and we particularly address the two-view image case." "We address this problem using a lightweight edge-aware GAN network, that performs unpaired image-to-image translation while maintaining the disparity values."

Key Insights Distilled From

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

by Vasudha Venk... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09277.pdf

Deeper Inquiries

How can the proposed edge-aware translation approach be extended to handle more complex scene structures and occlusions in remote sensing data

The proposed edge-aware translation approach can be extended to handle more complex scene structures and occlusions in remote sensing data by incorporating advanced edge detection techniques and leveraging contextual information. To address complex scene structures, more sophisticated edge detection algorithms such as Canny edge detection or deep learning-based edge detectors can be employed to capture intricate details and boundaries accurately. These algorithms can help in identifying edges of objects with irregular shapes or fine details, enhancing the structural information retained during translation. Moreover, the model can be enhanced to handle occlusions by integrating occlusion-aware techniques that can identify and preserve occluded regions in the images. This can involve incorporating occlusion masks or depth information to guide the translation process, ensuring that occluded areas are accurately represented in the translated images. By considering occlusions in the translation process, the model can maintain the spatial relationships between objects even in challenging scenarios with occluded regions. By combining advanced edge detection methods and occlusion-aware techniques, the edge-aware translation approach can effectively handle complex scene structures and occlusions in remote sensing data, improving the overall quality and accuracy of the translated images.

What other geometric constraints, beyond stereo, could be incorporated to further improve the semantic consistency of the translated images

In addition to stereo constraints, incorporating other geometric constraints can further enhance the semantic consistency of the translated images in remote sensing data. One key geometric constraint that can be integrated is vanishing point alignment. By aligning the vanishing points in the translated images, the model can ensure that the perspective and spatial relationships between objects remain consistent across domains. This alignment can help in maintaining the geometric accuracy of the scenes, especially in aerial imagery where perspective plays a crucial role in scene interpretation. Furthermore, integrating constraints related to scale and proportion can improve the semantic consistency of the translated images. By preserving the relative sizes and proportions of objects in the scene, the model can ensure that the spatial layout and context remain coherent in the translated images. This can be particularly beneficial in remote sensing applications where accurate representation of object sizes and spatial relationships is essential for downstream tasks such as object detection and classification. By incorporating vanishing point alignment, scale, and proportion constraints alongside stereo constraints, the edge-aware translation approach can achieve a higher level of semantic consistency in the translated images, enhancing their usability and interpretability in remote sensing applications.

Can the disentanglement of content and style be leveraged to enable few-shot adaptation of the translation model to new remote sensing domains

The disentanglement of content and style in the translation model can indeed facilitate few-shot adaptation to new remote sensing domains by enabling the transfer of learned content representations while adjusting the style to match the characteristics of the new domain. This disentanglement allows for the separation of domain-specific features (style) from domain-agnostic features (content), making it easier to adapt the model to new domains with minimal data requirements. During few-shot adaptation, the content representations learned from previous domains can be retained, while the style representations can be fine-tuned or adjusted based on the characteristics of the new domain. This process enables the model to quickly adapt to the visual attributes and structural elements specific to the new remote sensing domain, enhancing its generalization capabilities. By leveraging the disentangled content and style representations, the translation model can efficiently adapt to new remote sensing domains with limited data, making it a versatile and adaptable solution for a wide range of applications in the field of remote sensing image-to-image translation.