A bayesian approach to uncertainty-based depth map super resolution

Authors:
Jing Li;Gang Zeng;Rui Gan;Hongbin Zha;Long Wang
Affiliations:
Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China;Key Laboratory of Machine Perception, Peking University, Beijing, China
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Year:
2012

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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.