Building Illumination Coherent 3D Models of Large-Scale Outdoor Scenes

Abstract

Systems for the creation of photorealistic models using range scans and digital photographs are becoming increasingly popular in a wide range of fields, from reverse engineering to cultural heritage preservation. These systems employ a range finder to acquire the geometry information and a digital camera to measure color detail. But bringing together a set of range scans and color images to produce an accurate and usable model is still an area of research with many unsolved problems. In this paper we address the problem of how to build illumination coherent integrated texture maps from images that were taken under different illumination conditions. To achieve this we present two different solutions. The first one is to align all the images to the same illumination, for which we have developed a technique that computes a relighting operator over the area of overlap of a pair of images that we then use to relight the entire image. Our proposed method can handle images with shadows and can effectively remove the shadows from the image, if required. The second technique uses the ratio of two images to factor out the diffuse reflectance of an image from its illumination. We do this without any light measuring device. By computing the actual reflectance we remove from the images any effects of the illumination, allowing us to create new renderings under novel illumination conditions.

Integrating LIDAR Range Scans and Photographs with Temporal Changes

“…While this does help to blend the images, seams are still visible if the lighting changes too much. A more sophisticated method such as that presented in the work of Troccoli et al [23] could help improve this.…”

Coherent intrinsic images from photo collections

“…Troccoli and Allen [2008] use a laser scan and multiple lighting and viewing conditions to perform relighting and estimate Lambertian reflectance. In addition to a detailed geometry, they rely on a user-assisted shadow detector.…”

“…Inspired by existing work on time-lapse sequences [Weiss 2001;Matsushita et al 2004], we consider these variations as a rich source of information to compute intrinsic images, i.e., to decompose photos into the product of an illumination layer by a reflectance layer [Barrow and Tenenbaum 1978]. This decomposition is an ill-posed problem since an infinity of reflectance and illumination configurations can produce the same image, and so far automatic techniques are limited to simple objects [Grosse et al 2009], while real-world scenes require user assistance [Bousseau et al 2009], detailed geometry [Troccoli and Allen 2008;Haber et al 2009], or varying illumination with a fixed or restricted viewpoint [Weiss 2001;Liu et al 2008].…”

Color matching and illumination estimation for urban scenes

“…A third approach [10], used in the video conferencing domain, learns a color mapping function for human faces and then applies this mapping to the whole image. Some other approaches [2,7,16] learn an consistent color from multiple images or multiple parts of an image for 3D scenes. In addition, the problem of learning a good color mapping function from a large image database [9] has been studied outside the 4D scene visualization context.…”