ParisStreetView-RandomMasks: Large-Scale Urban Image Inpainting Dataset with Random Irregular Masks
Abstract
ParisStreetView-RandomMasks ParisStreetView-RandomMasks is a large-scale urban image inpainting dataset designed for computer vision researchers, generative AI developers, and deep learning practitioners working on image restoration and scene completion tasks. The dataset contains 22,601 street-view images along with synthetically generated irregular random masks and corresponding corrupted images for image inpainting research. Dataset Contents Original urban street-view images Random irregular free-form masks Corrupted images for inpainting tasks Metadata annotations Training and validation split support Dataset Structure Dataset/ ├── images/ ├── masks/ ├── corrupted/ ├── train.txt ├── val.txt └── annotations.csv Applications Image Inpainting Diffusion Model Training Scene Completion Generative AI Computer Vision Research Image Restoration Deep Learning Benchmarking Mask Generation The masks were generated using irregular free-form random stroke simulation methods with varying thickness and region complexity to create realistic missing regions for supervised inpainting training. Recommended Models LaMa Partial Convolution Stable Diffusion Inpainting EdgeConnect Context Encoder Research Domains Computer Vision Generative AI Deep Learning Image Processing Scene Understanding Acknowledgement This work is based on the original Paris StreetView dataset. Additional preprocessing, random mask generation, and corrupted image creation were performed for image inpainting and…
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