Name
Abstract | ||
|---|---|---|
We introduce a method that can register challenging images from specular and poorly textured 3D environments, on which previous approaches fail. We assume that a small set of reference images of the environment and a partial 3D model are available. Like previous approaches, we register the input images by aligning them with one of the reference images using the 3D information. However, these approaches typically rely on the pixel intensities for the alignment, which is prone to fail in presence of specularities or in absence of texture. Our main contribution is an efficient novel local descriptor that we use to describe each image location. We show that we can rely on this descriptor in place of the intensities to significantly improve the alignment robustness at a minor increase of the computational cost, and we analyze the reasons behind the success of our descriptor. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/CVPR.2014.436 | CVPR |
Keywords | Field | DocType |
image registration,image texture,solid modelling,3D tracking,computational cost,descriptor fields,image registration,local descriptor,partial 3D model,specular 3D environment,textured 3D environment,3D Tracking,Dense Descriptor,Image Registration,Specular Environment | Image alignment,Computer vision,Pattern recognition,GLOH,Computer science,Specular reflection,Robustness (computer science),Pixel,Artificial intelligence,3d tracking,Small set,Image registration | Conference |
ISSN | Citations | PageRank |
1063-6919 | 18 | 0.54 |
References | Authors | |
23 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Crivellaro | 1 | 44 | 2.14 |
| Vincent Lepetit | 2 | 6178 | 306.48 |