Name
Abstract | ||
|---|---|---|
The objective of this paper is to increase both spacial resolution and depth precision of a depth map. Our work aims to produce a super resolution depth map with quality as well as precision. This paper is motivated by the fact that errors of depth measurements from the sensor are inherent. By combining prior geometry of the scene, we propose a Bayesian approach to the uncertainty-based depth map super resolution. In particular, uncertainty of depth measurements is modeled in terms of kernel estimation and is used to formulate the likelihood. In this paper, we incorporate a gauss kernel on depth direction as well as an anisotropic spatial-color kernel. We further utilize geometric assumptions of the scene, namely the piece-wise planar assumption, to model the prior. Experiments on different datasets demonstrate effectiveness and precision of our algorithm compared with the state-of-art. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-37447-0_16 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
depth direction,spacial resolution,depth precision,super resolution depth map,depth measurement,anisotropic spatial-color kernel,depth map,gauss kernel,kernel estimation,uncertainty-based depth,bayesian approach | Kernel (linear algebra),Computer vision,Gauss,Pattern recognition,Markov random field,Computer science,Mean squared error,Planar,Artificial intelligence,Depth map,Bayesian probability,Kernel density estimation | Conference |
Volume | Issue | ISSN |
7727 LNCS | PART 4 | 16113349 |
Citations | PageRank | References |
1 | 0.51 | 17 |
Authors | ||
5 | ||