Name
Abstract | ||
|---|---|---|
We present a method for computing ambient occlusion (AO) for a stack of images of a scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attention in vision, we show that it can be approximated using simple, per-pixel statistics over image stacks, based on a simplified image formation model. We use our derived AO measure to compute reflectance and illumination for objects without relying on additional smoothness priors, and demonstrate state-of-the art performance on the MIT Intrinsic Images benchmark. We also demonstrate our method on several synthetic and real scenes, including 3D printed objects with known ground truth geometry. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CVPR.2013.325 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
computational geometry,computer graphics,lighting,natural scenes,3D printed objects,AO computing,MIT intrinsic image benchmark,computer graphics,ground truth geometry,image formation model,image stacking,objects illumination,per-pixel statistics,photometric ambient occlusion,reflectance,vision attention,albedo,ambient occlusion,image stacks,intrinsic images | Computer vision,Visibility,Screen space ambient occlusion,Computer science,Computational geometry,Image formation,Ground truth,Ambient occlusion,Artificial intelligence,Smoothness,Computer graphics | Conference |
ISSN | Citations | PageRank |
1063-6919 | 9 | 0.47 |
References | Authors | |
14 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel C. Hauagge | 1 | 19 | 1.49 |
| Scott Wehrwein | 2 | 17 | 2.29 |
| Kavita Bala | 3 | 2046 | 138.75 |
| Noah Snavely | 4 | 4262 | 197.04 |