Paper Info
Title
Shadow Detection and Sun Direction in Photo Collections
Abstract
Modeling the appearance of outdoor scenes from photo collections is challenging because of appearance variation, especially due to illumination. In this paper we present a simple and robust algorithm for estimating illumination properties-shadows and sun direction-from photo collections. These properties are key to a variety of scene modeling applications, including outdoor intrinsic images, realistic 3D scene rendering, and temporally varying (4D) reconstruction. Our shadow detection method uses illumination ratios to analyze lighting independent of camera effects, and determines shadow labels for each 3D point in a reconstruction. These shadow labels can then be used to detect shadow boundaries and estimate sun direction, as well as to compute dense shadow labels in pixel space. We demonstrate our method on large Internet photo collections of scenes, and show that it outperforms prior multi-image shadow detection and sun direction estimation methods.
Year
DOI
Venue
2015
10.1109/3DV.2015.58
3DV
Keywords
Field
DocType
appearance variation,illumination properties,outdoor scene appearance modeling applications,outdoor intrinsic images,realistic 3D scene rendering,temporally varying reconstruction,shadow detection method,illumination ratio,lighting,camera effects,3D point,sun direction,pixel space,Internet photo collections
Iterative reconstruction,Shadow,Computer vision,Computer graphics (images),Computer science,Artificial intelligence,Pixel,Rendering (computer graphics)
Conference
Citations 
PageRank 
References 
4
0.38
21
Authors
3
Name
Order
Citations
PageRank
Scott Wehrwein1172.29
Kavita Bala22046138.75
Noah Snavely34262197.04