Abstract

The appearance of an outdoor scene depends on a variety of factors such as viewing geometry, scene structure and reffiectance (BRDF or BTF), illumination (sun, moon, stars, street lamps), atmospheric con- dition (clear air, fog, rain) and weathering (or aging) of materials. Over time, these factors change, altering the way a scene appears. A large set of images is required to study the entire variability in scene appearance. In this paper, we present a database of high quality registered and cal- ibrated images of a fixed outdoor scene captured every hour for over 5 months. The dataset covers a wide range of daylight and night illumi- nation conditions, weather conditions and seasons. We describe in detail the image acquisition and sensor calibration procedures. The images are tagged with a variety of ground truth data such as weather and illumi- nation conditions and actual scene depths. This database has potential implications for vision, graphics, image processing and atmospheric sci- ences and can be a testbed for many algorithms. We describe an example application - image analysis in bad weather - and show how this method can be evaluated using the images in the database. The database is avail- able online at http://www.cs.columbia.edu/CAVE/. The data collection is ongoing and we plan to acquire images for one year.