Places in the Wild: A Large, High-Resolution RAW Photograph Dataset for Ecologically Valid Vision Research
2026-06-01 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created a new dataset called Places in the Wild, featuring over 67,000 very high-resolution photos taken directly at 810 real-world locations. Unlike most image datasets made from internet pictures, these photos capture complete 360-degree views with precise details like lighting and color because they were taken with a special camera setup. The dataset includes extra information about how each photo was taken and quality measures, making it useful for studying how humans and computers recognize scenes from different viewpoints. This resource helps researchers understand natural environments better and improve computer vision models in realistic settings.
image datasethigh-resolution photography360-degree imagingRAW image formatluminancecontrastscene recognitioncomputer visionEXIF metadatanatural scene statistics
Authors
Michelle R. Greene
Abstract
Large image datasets have accelerated progress in cognitive neuroscience and computer vision. However, most datasets are low-resolution, internet-sourced JPEGs with unknown capture conditions and limited spatial context. Places in the Wild is a dataset of 67,574 high-resolution photographs collected in situ across 810 physical locations spanning 260 basic-level scene categories, including indoor, urban, and natural environments. At each location, a 45-megapixel Canon EOS R5 mounted on a panoramic tripod captured 72 images at 5-degree horizontal intervals plus 12 images at varying elevations, yielding dense 360-degree viewpoint sampling. All images were recorded simultaneously as 14-bit RAW (CR3) files and compressed JPEGs, preserving sensor-level detail for analyses of luminance, contrast, color, and other image statistics. The dataset is accompanied by complete EXIF metadata and a suite of image-quality metrics. Places in the Wild supports research on viewpoint-dependent recognition in humans and models, training and evaluation of scene-understanding systems under realistic conditions, characterization of natural scene statistics, and experiments requiring near-full-field visual displays.