Abstract. The appearance of an outdoor scene depends on a variety of factors such as viewing geometry, scene structure and reflectance (BRDF or BTF), illumination (sun, moon, stars, street lamps), atmospheric condition (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 calibrated images of a fixed outdoor scene captured every hour for over 5 months. The dataset covers a wide range of daylight and night illumination 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 illumination conditions and actual scene depths. This database has potential implications for vision, graphics, image processing and atmospheric sciences 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 available online at http://www.cs.columbia.edu/CAVE/. The data collection is ongoing and we plan to acquire images for one year. The appearance of a fixed scene depends on several factors -the viewing geometry, illumination geometry and spectrum, scene structure and reflectance (BRDF or BTF) and the medium (say, atmosphere) in which the scene is immersed. The estimation of one or more of these appearance parameters from one or more images of the scene has been an important part of research in computer vision. Several researchers have focused on solving this inverse problem under specific conditions of illumination (constant or smoothly varying), scene structure (no discontinuities), BRDF (lambertian) and transparent media (pure air). Images captured to evaluate their methods adhere to the specific conditions. While understanding each of these specific cases is important, modeling scene appearance in the most general setting is ultimately the goal of a vision system. To model, develop and evaluate such a general vision system, it is critical to collect a comprehensive set of images that describes the complete variability in the appearance of a scene. Several research groups have collected images of a scene (for example, faces, textures, objects) under varying lighting conditions
Separate illuminations have been provided for outdoor scenes with changing daylight and weather conditions REF .
All the images of an outdoor scene
Proc. European Conf. Computer Vision
Computer Science